Repositories

• ahaque/arrhythmia-nn - [[Paper](http://cs229.stanford.edu/proj2014/Albert%20Haque,%20Cardiac%20Dysrhythmia%20Detection%20with%20GPU-Accelerated%20Neural%20Networks.pdf)] - Neural network for detecting cardiac dysrhythmia
• awni/ecg - Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
• limagbz/arrhythmia-alarms - Detecting false arrhythmia alarms in the ICU using Convolutional Neural Networks
• akshaynathr/deep_heart_hackatho - Anomaly detection system for heart diseases from ECG using machine learning
• SajadMo/ECG-Heartbeat-Classification-seq2seq-model - Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
• nicolebrimmer/senior-thesis - Predicting Ventricular Tachycardia Using LSTMs and ECG Signals
• sbasu26/Predicting-Heart-Disease-using-ANN - Heart disease prediction using various approaches with Keras and Tensorflow
• SreehariRamMohan/Heart-Sounds-Deep-Learning - Diagnose heart arrhythmias using deep learning using very low cost equipment
• Vikashtripathi/Deep-learning-for-heart-disease-prediction - Deep neural network model for heart disease prediction using 10 practical datapoints
• nerdySingh/LifeTech - life.tech, a comprehensive system that can detect cardiac abnormalities using machine learning and low cost hardware with emergency SMS notifications
• sachanganesh/heartbeat-classification - Deep Stethoscope: CNN Heartbeat Classifier
• pablocarreraflorez/heartbeat-deep-learning - Experiments in deep learning with heartbeat signals derived from the MIT-BIH Arrhythmia Dataset and The PTB Diagnostic ECG Database
• Atharvious/Predicting-Heart-Disease - Prediction of heart disease using a deep learning neural network

Datasets

• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Kachuee-Fazeli-Sarrafzadeh ECG Heartbeat Categorization Dataset
• MIT-BIH Arrhythmia Database
• MIT-BIH Atrial Fibrillation Database
• CinC Challenge 2016: Heart Sound Training Sets
• PhysioNet Database Collection
• Janosi-Steinbrunn-Pfisterer-Detrano Heart Disease Data Set
• DeepQ Arrhythmia Database: A Large-Scale Dataset for Arrhythmia Detector Evaluation - Meng-Hsi Wu, Edward Y. Chang (2017)
• Blood Vessel Geometry Synthesis using Generative Adversarial Networks - Jelmer M. Wolterink, Tim Leiner, Ivana Isgum (2018)
• Generating Multi-label Discrete Patient Records using Generative Adversarial Networks - Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F. Stewart, Jimeng Sun (2018)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)
• Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)

Tutorials

• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)
• ECG arrhythmia classification using a 2-D convolutional neural network - Ankur Singh (2018)
• Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM - Andrew Long (2019)

• Cardiac segmentation - ecg) at Papers With Code

Tutorials

• Classifying Heart Sounds Challenge - 2012)
• Classifying Heart Sounds Challenge - 2012)
• Classification of Normal/Abnormal Heart Sound Recordings: the PhysioNet/Computing in Cardiology Challenge
• AF Classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge

Meta reviews

• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Deep Learning in Cardiology - Paschalis Bizopoulos, Dimitrios Koutsouris (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)
• Artificial intelligence for precision oncology: beyond patient stratification - Francisco Azuaje (2019)

Diagnostics

• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Machine Learning Approaches in Cardiovascular Imaging - Mir Henglin, Gillian Stein, Pavel V. Hushcha, Jasper Snoek, Alexander B. Wiltschko, Susan Cheng (2017)
• Deep Learning for Cardiac MRI: The Time Has Come - Patrick M. Colletti (2018)
• Artificial intelligence and echocardiography - M Alsharqi, W J Woodward, J A Mumith, PhD, D C Markham, R Upton, P Leeson (2018)
• Robust Heartbeat Detection From Multimodal Data via CNN-Based Generalizable Information Fusion - B. S. Chandra, C. S. Sastry, S. Jana (2018)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Heart Smart: A Novel Deep Learning Approach to Improving Heart Disease Diagnosis - Sofia Tomov (2018)
• A Computer Vision Pipeline for Automated Determination of Cardiac Structure and Function and Detection of Disease by Two-Dimensional Echocardiography - Jeffrey Zhang, Sravani Gajjala, Pulkit Agrawal, Geoffrey H. Tison, Laura A. Hallock, Lauren Beussink-Nelson, Eugene Fan, Mandar A. Aras, ChaRandle Jordan, Kirsten E. Fleischmann, Michelle Melisko, Atif Qasim, Alexei Efros, Sanjiv J. Shah, Ruzena Bajcsy, Rahul C. Deo (2018)
• Automated cardiovascular magnetic resonance image analysis with fully convolutional networks - Wenjia Bai, Matthew Sinclair, Giacomo Tarroni, Ozan Oktay, Martin Rajchl, Ghislain Vaillant, Aaron M. Lee, Nay Aung, Elena Lukaschuk, Mihir M. Sanghvi, Filip Zemrak, Kenneth Fung, Jose Miguel Paiva, Valentina Carapella, Young Jin Kim, Hideaki Suzuki, Bernhard Kainz, Paul M. Matthews, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Ben Glocker, Daniel Rueckert (2018)
• On Deep Neural Networks for Detecting Heart Disease - Nathalie-Sofia Tomov, Stanimire Tomov (2018)
• Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging - SJ Al'Aref, K Anchouche, G Singh, PJ Slomka, KK Kolli, A Kumar, M Pandey, G Maliakal, AR van Rosendael, AN Beecy, DS Berman, J Leipsic, K Nieman, D Andreini, G Pontone, UJ Schoepf, LJ Shaw, HJ Chang, J Narula, JJ Bax, Y Guan, JK Min (2019)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A Neural Network System for Detection of Atrial Fibrillation in Ambulatory Electrocardiograms - David Cubanski, David Cyganski, Elliott M. Antman, Charles L. Feldman (1994)
• Cardiac arrhythmia classification using neural networks - H. Al-Nashash (2000)
• Classification of arrhythmia using machine learning techniques - Thara Soman, Patrick O. Bobbie (2004)
• Neural network based arrhythmia classification using Heart Rate Variability signal - Babak Mohammadzadeh Asl, Seyed Kamaledin Setarehdan (2006)
• Artificial Neural Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG Signal Data - Shivajirao M. Jadhav, Sanjay L. Nalbalwar, Ashok A. Ghatol (2012)
• Automated detection & classification of arrhythmias - Richard Tang, Saurabh Vyas (2014)
• Cardiac Dysrhythmia Detection with GPU-Accelerated Neural Networks - [[Code](https://github.com/ahaque/arrhythmia-nn)] - Albert Haque (2014)
• Deep learning approach for active classification of electrocardiogram signals - M. M. Al Rahhal, Yakoub Bazi, Haikel AlHichria, NaifAlajlan, Farid Melgani, R. R.Yager (2016)
• Artificial intelligence classification methods of atrial fibrillation with implementation technology - Huey WOAN Lim, Yuan Wen Hau, Chiao Wen Lim, Mohd Afzan Othman (2016)
• Deep learning algorithm for arrhythmia detection - Hilmy Assodiky, Iwan Syarif, Tessy Badriyah (2017)
• Cardiologist-Level Arrhythmia Detection With Convolutional Neural Networks - Pranav Rajpurkar, Awni Hannun, Masoumeh Haghpanahi, Codie Bourn, and Andrew Ng (2017)
• Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances - Aurore Lyon, Ana Minchole, Juan Pablo Martinez, Pablo Laguna, Blanca Rodriguez (2017)
• Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG - Fernando Andreotti, Oliver Carr, Marco A F Pimentel, Maarten de Vos (2017)
• Arrhythmia Evaluation in Wearable ECG Devices - Muammar Sadrawi , Chien-Hung Lin, Yin-Tsong Lin, Yita Hsieh, Chia-Chun Kuo, Jen Chien Chien, Koichi Haraikawa, Maysam F. Abbod, Jiann-Shing Shieh (2017)
• Classification of ECG Arrhythmia with Machine Learning Techniques - Halil Ibrahim, Nese Usta, Musa Yildiz (2017)
• Deep learning algorithm for arrhythmia detection - Hilmy Assodiky, Iwan Syarif, Tessy Badriyah (2017)
• Cardiac arrhythmia detection using deep learning - Ali Isina, Selen Ozdalilib (2017)
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• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
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• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
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• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
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• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
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• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
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• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
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• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
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• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
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• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Ontology based congenital heart disease diagnosis using neural networks - A. Nirmala, Abinaya Sambath Kumar (2015)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Classification of arrhythmia using machine learning techniques - Thara Soman, Patrick O. Bobbie (2004)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records - Tomás Teijeiro, Constantino A. García, Daniel Castro, Paulo Félix (2017)
• Arrhythmia Detection Using Deep Convolutional Neural Network With Long Duration ECG Signals - Özal yıldırım, Paweł Pławiak, Ru San Tan, U Rajendra Acharya (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease - Andreas Hauptmann, Simon Arridge, Felix Lucka, Vivek Muthurangu, Jennifer A. Steeden (2018)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease - Jelmer M. Wolterink, Tim Leiner, Max A. Viergever, Ivana Išgum (2017)
• Deep-learning models improve on community-level diagnosis for common congenital heart disease lesions - Rima Arnaout, Lara Curran, Erin Chinn, Yili Zhao, Anita Moon-Grady (2018)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Carotid Artery Characterization in Ultrasound Imaging using Machine Learning Techniques - Maria del Mar Vila (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Machine learning for nuclear cardiology: The way forward - Sirish Shrestha, Partho P. Sengupta (2018)
• Machine Learning in the Evaluation of Myocardial Ischemia Through Nuclear Cardiology - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)
• Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)
• A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)
• Cardiac Arrhythmia Classification Using Machine Learning Techniques - Namrata Singh, Pradeep Singh (2018)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)
• Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation - Saman Parvaneh, Jonathan Rubin, Asif Rahman, Bryan Conroy, Saeed Babaeizadeh (2018)
• Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)
• An Attention-Based CNN for ECG Classification - Alexander Kuvaev, Roman Khudorozhkov (2019)
• Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)
• Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals - Paweł Pławiak, U. Rajendra Acharya (2019)
• Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)

Tutorials

• Artificial Intelligence in Precision Cardiovascular Medicine - Chayakrit Krittanawong, HongJu Zhang, Zhen Wang, Mehmet Aydar, Takeshi Kitai (2017)
• Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions - Brian C. S. Loh, Patrick H. H. Then (2017)
• Artificial Intelligence in Cardiology - Kipp W. Johnson, Jessica Torres Soto, Benjamin S. Glicksberg, Khader Shameer, Riccardo Miotto, Mohsin Ali, Euan Ashley, Joel T. Dudley (2018)
• Machine learning in cardiovascular medicine: are we there yet? - Khader Shameer, Kipp W Johnson, Benjamin S Glicksberg, Joel T Dudley, Partho P Sengupta (2018)
• Deep learning for cardiovascular medicine: a practical primer - Chayakrit Krittanawong, Kipp W Johnson, Robert S Rosenson, Zhen Wang, Mehmet Aydar, Usman Baber, James K Min, W H Wilson Tang, Jonathan L Halperin, Sanjiv M Narayan (2019)
• Blood Pressure Classification Using the Method of the Modular Neural Networks - Martha Pulido, Patricia Melin, German Prado-Arechiga (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Deep learning for cardiovascular medicine: a practical primer - Chayakrit Krittanawong, Kipp W Johnson, Robert S Rosenson, Zhen Wang, Mehmet Aydar, Usman Baber, James K Min, W H Wilson Tang, Jonathan L Halperin, Sanjiv M Narayan (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)
• Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram - Zachi I. Attia, Suraj Kapa, Francisco Lopez-Jimenez, Paul M. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, Maurice Enriquez-Sarano, Peter A. Noseworthy, Thomas M. Munger, Samuel J. Asirvatham, Christopher G. Scott, Rickey E. Carter, Paul A. Friedman (2019)

Techniques

• Super-resolution reconstruction of cardiac MRI using coupled dictionary learning - Kanwal K. Bhatia, Anthony N. Price, Wenzhe Shi, Jo V. Hajnal, Daniel Rueckert (2014)
• Designing Neuromorphic Computing Systems with Memristor Devices - Amr Mahmoud (2019)

Prediction

• Noninvasive diagnosis of coronary artery disease using a neural network algorithm - Metin Akay (1992)
• Machine Learning Data Imputation and Classification in a Multicohort Hypertension Clinical Study - William Seffens, Chad Evans, Taylor Minority Health-Grid Network And Herman (2016)
• Artificial Intelligence in Mitral Valve Analysis - Jelliffe Jeganathan, Ziyad Knio, Yannis Amador, Ting Hai, Arash Khamooshian, Robina Matyal, Kamal R Khabbaz, Feroze Mahmood (2017)
• Improving the value of clinical variables in the assessment of cardiovascular risk using Artificial Neural Networks - L.E. Juarez-Orozco R.J.J. Knol C.A. Sanchez-Catasus F.M. Van Der Zant J. Knuuti (2017)
• DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction - Brandon Ballinger, Johnson Hsieh, Avesh Singh, Nimit Sohoni, Jack Wan, Geoffrey H. Tison, Gregory M. Marcus, Jose M. Sanchez, Carol Maguire Jeffrey E. Olgin, Mark J. Pletcher (2018)
• Deep learning in quantitative PET myocardial perfusion imaging to predict adverse cardiovascular events - L E Juarez-Orozco O Martinez-Manzanera F M Van Der Zant R J J Knol J Knuuti (2019)
• A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records - Zhengxing Huang, Wei Dong, Huilong Duan, Jiquan Liu (2017)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Pre-Diagnosis of Hypertension Using Artificial Neural Network - B. Sumathi, A. Santhakumaran (2011)
• Anthropometric Predictors and Artificial Neural Networks in the diagnosis of Hypertension - Krzysztof Pytel, Tadeusz Nawarycz, Wojciech Drygas, Lidia Ostrowska-Nawarycz (2015)
• Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data - Ramin Mohammadi, Sarthak Jain, Stephen Agboola, Ramya Palacholla, Sagar Kamarthi, Byron C. Wallace (2019)
• A novel multi-class approach for early-stage prediction of sudden cardiac death - Reeta Devi, Hitender Kumar Tyagi, Dinesh Kumar (2019)
• Predicting Mortality after Coronary Artery Bypass Surgery: What Do Artificial Neural Networks Learn? - Jack V Tu, Milton C. Weinstein, Barbara J Mcneil, C. David Naylor (1998)
• The determination of cardiac surgical risk using artificial neural networks - Dan A. Buzatu, Kim K. Taylor, Daniel C. Peret, Nicholas P. Lang (2001)
• Weight-elimination Neural Networks Applied to Coronary Surgery Mortality Prediction - Colleen M. Ennett, Monique Frize (2002)
• Risk factor identification and mortality prediction in cardiac surgery using artificial neural networks - 3/fulltext) - Johan Nilsson, Mattias Ohlsson, Lars Thulin, Peter Höglund, Samer A.M. Nashef, Johan Brandt (2006)
• Predicting adverse outcomes of cardiac surgery with the application of artificial neural networks - S. Y. Peng, S. K. Peng (2008)
• Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables - Rocco J. LaFaro, Suryanarayana Pothula, Keshar Paul Kubal, Mario Emil Inchiosa, Venu M. Pothula, Stanley C. Yuan, David A. Maerz, Lucresia Montes, Stephen M. Oleszkiewicz, Albert Yusupov, Richard Perline, Mario Anthony Inchiosa, Jr. (2015)
• Predicting reintubation, prolonged mechanical ventilation and death in post-coronary artery bypass graft surgery: a comparison between artificial neural networks and logistic regression models - Renata G. Mendes, César R. de Souza, Maurício N. Machado, Paulo R. Correa, Luciana Di Thommazo-Luporini, Ross Arena, Jonathan Myers, Ednaldo B. Pizzolato, Audrey Borghi-Silva (2015)
• Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System - Hamidreza Maharlou, Sharareh R. Niakan Kalhori, Shahrbanoo Shahbazi, Ramin Ravangard (2018)
• Frailty and cardiovascular surgery, deep neural network versus support vector machine to predict death - Rashmee Shah, Yijun Shao, Kristina Doing-Harris, Charlene Weir, Yan Cheng, Bruce Bray, Qing Zeng (2018)
• Machine learning techniques in cardiac risk assessment - Elif Kartal, Mehmet Erdal Balaban (2018)
• Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients - Shinichi Goto, Mai Kimura, Yoshinori Katsumata, Shinya Goto, Takashi Kamatani, Genki Ichihara, Seien Ko, Junichi Sasaki, Keiichi Fukuda, Motoaki Sano (2019)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis - Bharath Ambale-Venkatesh, Xiaoying Yang, Colin O. Wu, Kiang Liu, W. Gregory Hundley, Robyn McClelland, Antoinette S. Gomes, Aaron R. Folsom, Steven Shea, Eliseo Guallar, David A. Bluemke, João A.C. Lima (2017)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest - Joon‐myoung Kwon, Youngnam Lee, Yeha Lee, Seungwoo Lee, Jinsik Park (2018)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Artificial Neural Networks Prognostic Evaluation of Post-Surgery Complications in Patients Underwent to Coronary Artery Bypass Graft Surgery - Cesar Roberto De Souza, Ednaldo Brigante Pizzolato, Renata Gonçalves Mendes, Paulo Correa (2009)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction - Brandon Ballinger, Johnson Hsieh, Avesh Singh, Nimit Sohoni, Jack Wan, Geoffrey H. Tison, Gregory M. Marcus, Jose M. Sanchez, Carol Maguire Jeffrey E. Olgin, Mark J. Pletcher (2018)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data - Ramin Mohammadi, Sarthak Jain, Stephen Agboola, Ramya Palacholla, Sagar Kamarthi, Byron C. Wallace (2019)
• Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System - Hamidreza Maharlou, Sharareh R. Niakan Kalhori, Shahrbanoo Shahbazi, Ramin Ravangard (2018)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)
• Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area - Shuqiong Huang, Yihua Xu, Li Yue, Sheng Wei, Li Liu, Xiumin Gan, Shuihong Zhou, Shaofa Nie - (2010)

Prognostics

• Machine learning improves the long-term prognostic value of sequential cardiac PET/CT - L E Juarez-Orozco T Maaniitty O Martinez-Manzanera A Saraste J Knuuti (2018)
• A deep neural network to enhance prediction of 1-year mortality using echocardiographic videos of the heart - Alvaro Ulloa, Linyuan Jing, Christopher W Good, David P vanMaanen, Sushravya Raghunath, Jonathan D Suever, Christopher D Nevius, Gregory J Wehner, Dustin Hartzel, Joseph B Leader, Amro Alsaid, Aalpen A Patel, H Lester Kirchner, Marios S Pattichis, Christopher M Haggerty, Brandon K Fornwalt (2018)
• Machine Learning Prediction of Response to Cardiac Resynchronization Therapy - Albert K. Feeny, John Rickard, Divyang Patel, Saleem Toro, Kevin M. Trulock, Carolyn J. Park, Michael A. LaBarbera, Niraj Varma, Mark J. Niebauer, Sunil Sinha, Eiran Z. Gorodeski, Richard A. Grimm, Xinge Ji, John Barnard, Anant Madabhushi, David D. Spragg, Mina K. Chung (2019)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Use of Machine Learning to Accurately Predict Adverse Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data - Elsie Gyang Ross, Nigam Shah, Ronald Dalman, Kevin Nead, John Cooke, Nicholas J. Leeper (2017)
• Machine Learning Predicts Mortality Better Than Biomarker Staging in Wild-type Cardiac Amyloidosis - Avinainder Singh, Hallie I Geller, Rodney H Falk (2018)
• Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10,019 patients - Gerhard-Paul Diller, Aleksander Kempny, Sonya V Babu-Narayan, Marthe Henrichs, Margarita Brida, Anselm Uebing, Astrid E Lammers, Helmut Baumgartner, Wei Li, Stephen J Wort, Konstantinos Dimopoulos, Michael A Gatzoulis (2019)
• Artificial Intelligence Improves Accuracy of Heart Failure Readmission Risk Predictions - HealthCatalyst, Pulse Heart Institute, MultiCare (2018)
• Use of a neural network as a predictive instrument for length of stay in the intensive care unit following cardiac surgery - J. V. Tu, M. R. Guerriere (1992)
• Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery - Ramesh B. Manyam, Yanqing Zhang, William B. Keeling, Jose Binongo, Michael Kayatta, Seth Carter (2018)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study - Tian Chen, Pamela Brewster, Katherine R Tuttle, Lance D Dworkin, William Henrich, Barbara A Greco, Michael Steffes, Sheldon Tobe, Kenneth Jamerson, Karol Pencina, Joseph M Massaro, Ralph B D’Agostino, Sr, Donald E Cutlip, Timothy P Murphy, Christopher J Cooper, Joseph I Shapiro (2019)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine learning improves the long-term prognostic value of sequential cardiac PET/CT - L E Juarez-Orozco T Maaniitty O Martinez-Manzanera A Saraste J Knuuti (2018)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine learning improves the long-term prognostic value of sequential cardiac PET/CT - L E Juarez-Orozco T Maaniitty O Martinez-Manzanera A Saraste J Knuuti (2018)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers - Konrad Pieszko, Jarosław Hiczkiewicz, Paweł Budzianowski, Jan Budzianowski, Janusz Rzeźniczak, Karolina Pieszko, Paweł Burchardt (2019)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome – the MADDEC study - Jussi A. Hernesniemi, Shadi Mahdiani, Juho A. Tynkkynen, Leo-Pekka Lyytikäinen, Pashupati P. Mishra, Terho Lehtimäki, Markku Eskola, Kjell Nikus, Kari Antila, Niku Oksala (2019)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)
• Machine Learning Improves Risk Stratification After Acute Coronary Syndrome - Paul D. Myers, Benjamin M. Scirica, Collin M. Stultz (2017)

Simulation

• ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy Gradient - Fei Ye, Fei Zhu, Yuchen Fu, Bairong Shen (2019)
• Synthesis of Realistic ECG using Generative Adversarial Networks - (2019)

Identification

• Cardiac Rhythm Device Identification Using Neural Networks - James P. Howard, Louis Fisher, Matthew J. Shun-Shin, Daniel Keene, Ahran D. Arnold, Yousif Ahmad, Christopher M. Cook, James C. Moon, Charlotte H. Manisty, Zach I. Whinnett, Graham D. Cole, Daniel Rueckert, Darrel P. Francis (2019)
• Personal Identification by Convolutional Neural Network with ECG Signal - Jianbo Xu, Tianhui Li, Ying Chen, Wenxi Chen (2018)
• Finger ECG based Two-phase Authentication Using 1D Convolutional Neural Networks - Ying Chen, Wenxi Chen (2018)
• ECG Authentication Method Based on Parallel Multi-scale One-dimensional Residual Network with Center and Margin Loss - Yifan Chu, Haibin Shen, Kejie Huang (2019)
• ECG-based personal recognition using a convolutional neural network - Yue Zhang, Zhibo Xiao, Zhenhua Guo, Ziliang Wang (2019)
• A Study on User Recognition Using 2D ECG Image Based on Ensemble Networks for Intelligent Vehicles - Min-Gu Kim, Hoon Ko, Sung Bum Pan (2019)
• Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing - Brett K. Beaulieu-Jones , Zhiwei Steven Wu , Chris Williams , Ran Lee , Sanjeev P. Bhavnani , James Brian Byrd , Casey S. Greene (2019)

Treatment

• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Towards Identifying of Effective Personalized Antihypertensive Treatment Rules from Electronic Health Records Data Using Classification Methods: Initial Model - Anna Semakova, Nadezhda Zvartau, Klavdiya Bochenina, Aleksandra Konradi (2017)
• Machine learning of big data in gaining insight into successful treatment of hypertension - Gideon Koren, Galia Nordon, Kira Radinsky, Varda Shalev (2018)
• Artificial Neural Network Modeling of Sustained Antihypertensive Drug Delivery using Polyelectrolyte Complex based on Carboxymethyl-kappa-carrageenan and Chitosan as Prospective Carriers - Sonia Lefnaoui, Samia Rebouh, Mounir Bouhedda, Madiha M. Yahoum, Salah Hanini (2018)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)
• Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)

Treatment

• Development of a Novel Convolution Neural Network for Arrhythmia Classification (AI-ECG) - Sanjeev Bhavnani MD, Scripps Clinic
• Artificial Intelligence With Deep Learning and Genes on Cardiovascular Disease - National Cheng-Kung University Hospital

Treatment

• Cardiac MRI analysis @ MIT
• Arrhythmia detection @ Stanford University

Treatment

• Stanford computer scientists develop an algorithm that diagnoses heart arrhythmias with cardiologist-level accuracy - Taylor Kubota (2017)
• How AI based arrhythmia detector can explain its decisions - Artem Bachynskyi (2018)
• Machine learning overtakes humans in predicting death or heart attack - European Society of Cardiology (2019)
• Artificial Intelligence and Echocardiography - Akhil Narang, Roberto M. Lang (2019)
• Artificial Intelligence examining ECGs predicts irregular heartbeat, death risk - American Heart Association (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applications for Artificial Intelligence in Cardiovascular Imaging - Dave Fornell, Diagnostic and Interventional Cardiology (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)
• Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! - Dindin Meryll (2019)