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https://github.com/cbailes/awesome-ai-cardiology

Awesome resources for artificial intelligence in cardiology
https://github.com/cbailes/awesome-ai-cardiology
List: awesome-ai-cardiology
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Awesome resources for artificial intelligence in cardiology
Host: GitHub
URL: https://github.com/cbailes/awesome-ai-cardiology
Owner: cbailes
License: other
Created: 2019-07-16T23:27:36.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2022-04-18T06:25:20.000Z (over 2 years ago)
Last Synced: 2024-05-23T02:01:09.859Z (7 months ago)
Size: 368 KB
Stars: 65
Watchers: 6
Forks: 30
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

awesome-hrv - Github Awesome awesome-ai-cardiology
awesome-artificial-intelligence - awesome-ai-cardiology - Awesome resources for artificial intelligence in cardiology. (Medicine)
ultimate-awesome - awesome-ai-cardiology - Awesome resources for artificial intelligence in cardiology. (Other Lists / PowerShell Lists)
README

        # awesome-ai-cardiology

[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

Comprehensive list of awesome code, datasets, papers, and resources for AI/deep learning/machine learning/neural networks applied to cardiology.

Open access: all rights granted for use and re-use of any kind, by anyone, at no cost, under your choice of either the free MIT License or Creative Commons CC-BY International Public License.

© 2019 Craig Bailes ([@cbailes](https://github.com/cbailes) | [[email protected]](mailto:[email protected]))

## Index

* [Code](#code)

  + [Repositories](#repositories)

  + [Datasets](#datasets)

    - [Generation](#generation)

  + [Tutorials](#tutorials)

* [Challenges](#challenges)

* [Papers](#papers)

  + [Meta reviews](#meta-reviews)

  + [Techniques](#techniques)

    - [Imaging](#imaging)

    - [Neuromorphic classification](#neuromorphic-classification)

  + [Diagnostics](#diagnostics)

    - [Arrhythmias](#arrhythmias)

      * [Realtime analysis](#realtime-analysis)

      * [Signal optimization](#signal-optimization)

    - [Artery disease](#artery-disease)

      * [Atherosclerosis](#atherosclerosis)

      * [Carotid](#carotid)

      * [Coronary](#coronary)

      * [Peripheral](#peripheral)

    - [Congenital disease](#congenital-disease)

    - [Heart failure](#heart-failure)

    - [Myocardial infarction](#myocardial-infarction)

  + [Prediction](#prediction)

    - [Acute coronary syndrome](#acute-coronary-syndrome)

    - [Cardiopulmonary arrest](#cardiopulmonary-arrest)

    - [Hypertension](#hypertension)

    - [Sudden cardiac death](#sudden-cardiac-death)

    - [Surgery](#surgery)

    - [Revascularization](#revascularization)

  + [Prognostics](#prognostics)

    - [Acute coronary syndrome](#acute-coronary-syndrome-1)

    - [Artery disease](#artery-disease-1)

    - [Cardiac amyloidosis](#cardiac-amyloidosis)

    - [Heart failure](#heart-failure-1)

    - [Surgery](#surgery-1)    

  + [Identification](#identification)

    - [Devices](#devices)

    - [Individuals](#individuals)

  + [Treatment](#treatment)

    - [Acute coronary syndrome](#acute-coronary-syndrome-2)

    - [Pharmacology](#pharmacology)

      * [Efficacy](#efficacy)

      * [Drug delivery](#drug-delivery)

* [Clinical trials](#clinical-trials)

* [Teams](#teams)

* [News, meta resources, and other](#news--meta-resources--and-other-further-reading)

  

## Code

* [Cardiac segmentation](https://paperswithcode.com/task/cardiac-segmentation) and [electrocardiography](https://paperswithcode.com/area/medical/electrocardiography-ecg) at Papers With Code

### Repositories

* [ahaque/arrhythmia-nn](https://github.com/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](https://github.com/awni/ecg) - Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

* [limagbz/arrhythmia-alarms](https://github.com/limagbz/arrhythmia-alarms) - Detecting false arrhythmia alarms in the ICU using Convolutional Neural Networks

* [akshaynathr/deep_heart_hackatho](https://github.com/akshaynathr/deep_heart_hackatho) - Anomaly detection system for heart diseases from ECG using machine learning

* [SajadMo/ECG-Heartbeat-Classification-seq2seq-model](https://github.com/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](https://github.com/nicolebrimmer/senior-thesis) - Predicting Ventricular Tachycardia Using LSTMs and ECG Signals

* [pablocarreraflorez/heartbeat-deep-learning](https://github.com/pablocarreraflorez/heartbeat-deep-learning/blob/master/hearbeat.ipynb) - Experiments in deep learning with heartbeat signals derived from the MIT-BIH Arrhythmia Dataset and The PTB Diagnostic ECG Database

* [sbasu26/Predicting-Heart-Disease-using-ANN](https://github.com/sbasu26/Predicting-Heart-Disease-using-ANN) - Heart disease prediction using various approaches with Keras and Tensorflow

* [SreehariRamMohan/Heart-Sounds-Deep-Learning](https://github.com/SreehariRamMohan/Heart-Sounds-Deep-Learning) - Diagnose heart arrhythmias using deep learning using very low cost equipment

* [Atharvious/Predicting-Heart-Disease](https://github.com/Atharvious/Predicting-Heart-Disease) - Prediction of heart disease using a deep learning neural network

* [Vikashtripathi/Deep-learning-for-heart-disease-prediction](https://github.com/Vikashtripathi/Deep-learning-for-heart-disease-prediction) - Deep neural network model for heart disease prediction using 10 practical datapoints

* [nerdySingh/LifeTech](https://github.com/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](https://github.com/sachanganesh/heartbeat-classification) - Deep Stethoscope: CNN Heartbeat Classifier

### Datasets

* [Kachuee-Fazeli-Sarrafzadeh ECG Heartbeat Categorization Dataset](https://www.kaggle.com/shayanfazeli/heartbeat) @ Kaggle

* [MIT-BIH Arrhythmia Database](https://www.kaggle.com/taejoongyoon/mitbit-arrhythmia-database) @ Kaggle

* [MIT-BIH Atrial Fibrillation Database](https://physionet.org/physiobank/database/afdb/) 

* [CinC Challenge 2016: Heart Sound Training Sets](https://physionet.org/physiobank/database/challenge/2016/)

* [PhysioNet Database Collection](https://physionet.org/physiobank/database/)

* [Janosi-Steinbrunn-Pfisterer-Detrano Heart Disease Data Set](https://archive.ics.uci.edu/ml/datasets/Heart+Disease) @ UC Irvine Machine Learning Repository

* [DeepQ Arrhythmia Database: A Large-Scale Dataset for Arrhythmia Detector Evaluation](https://storage.googleapis.com/research_htc_com_assets/publications/ecg-mmh17.pdf) - Meng-Hsi Wu, Edward Y. Chang (2017)

#### Generation

* [Blood Vessel Geometry Synthesis using Generative Adversarial Networks](https://arxiv.org/abs/1804.04381) - Jelmer M. Wolterink, Tim Leiner, Ivana Isgum (2018)

* [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://arxiv.org/abs/1703.06490) - Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F. Stewart, Jimeng Sun (2018)

* [Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network](https://www.nature.com/articles/s41598-019-42516-z) - Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong Shen (2019)

### Tutorials

* [ECG arrhythmia classification using a 2-D convolutional neural network](https://medium.com/datadriveninvestor/ecg-arrhythmia-classification-using-a-2-d-convolutional-neural-network-33aa586bad67) - Ankur Singh (2018)

* [Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM](https://towardsdatascience.com/detecting-heart-arrhythmias-with-deep-learning-in-keras-with-dense-cnn-and-lstm-add337d9e41f) - Andrew Long (2019)

## Challenges

* [Classifying Heart Sounds Challenge](http://www.peterjbentley.com/heartchallenge/) (2011-2012)

* [Classification of Normal/Abnormal Heart Sound Recordings: the PhysioNet/Computing in Cardiology Challenge](https://physionet.org/challenge/2016/) (2016)

* [AF Classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge](https://physionet.org/challenge/2017/) (2017)

## Papers

* [Artificial Intelligence in Precision Cardiovascular Medicine](https://www.sciencedirect.com/science/article/pii/S0735109717368456) - Chayakrit Krittanawong, HongJu Zhang, Zhen Wang, Mehmet Aydar, Takeshi Kitai (2017)

* [Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions](http://mhealth.amegroups.com/article/view/17021/17339) - Brian C. S. Loh, Patrick H. H. Then (2017)

* [Artificial Intelligence in Cardiology](https://www.onlinejacc.org/content/71/23/2668) - 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?](https://www.ncbi.nlm.nih.gov/pubmed/29352006) - Khader Shameer, Kipp W Johnson, Benjamin S Glicksberg, Joel T Dudley, Partho P Sengupta (2018)

* [Deep learning for cardiovascular medicine: a practical primer](https://academic.oup.com/eurheartj/article-abstract/40/25/2058/5366208) - 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](https://www.hindawi.com/journals/ijhy/2019/7320365/) - Martha Pulido, Patricia Melin, German Prado-Arechiga (2019)

* [Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram](https://www.nature.com/articles/s41591-018-0240-2) - 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)

### Meta reviews

* [Artificial intelligence for precision oncology: beyond patient stratification](https://www.nature.com/articles/s41698-019-0078-1) - Francisco Azuaje (2019)

* [Deep Learning in Cardiology](https://arxiv.org/abs/1902.11122) - Paschalis Bizopoulos, Dimitrios Koutsouris (2019)

### Techniques

#### Imaging

* [Super-resolution reconstruction of cardiac MRI using coupled dictionary learning](http://kresttechnology.com/krest-academic-projects/krest-mtech-projects/ECE/MTech%20DSP%202015-16/MTech%20DSP%20BasePaper%202015-16/61.pdf) - Kanwal K. Bhatia, Anthony N. Price, Wenzhe Shi, Jo V. Hajnal, Daniel Rueckert (2014)

#### Neuromorphic classification

* [Designing Neuromorphic Computing Systems with Memristor Devices](http://d-scholarship.pitt.edu/id/eprint/36830) - Amr Mahmoud (2019)

### Diagnostics

* [Machine Learning Approaches in Cardiovascular Imaging](https://www.ahajournals.org/doi/pdf/10.1161/CIRCIMAGING.117.005614) - Mir Henglin, Gillian  Stein, Pavel  V.  Hushcha, Jasper  Snoek, Alexander  B.  Wiltschko, Susan  Cheng (2017)

* [Deep Learning for Cardiac MRI: The Time Has Come](https://pubs.rsna.org/doi/abs/10.1148/radiol.2018182107?journalCode=radiology) - Patrick M. Colletti (2018)

* [Artificial intelligence and echocardiography](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6280250/) - 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](https://www.researchgate.net/publication/326316725_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](https://link.springer.com/article/10.1007/s12350-018-1284-x) - Sirish Shrestha, Partho P. Sengupta (2018)

* [Heart Smart: A Novel Deep Learning Approach to Improving Heart Disease Diagnosis](https://www.googlesciencefair.com/projects/2018/f429f8ca25dd5f19ea86b22a4c53638cc675f2220b10419e0bb9883974c2a131) - Sofia Tomov  (2018)

* [A Computer Vision Pipeline for Automated Determination of Cardiac Structure and Function and Detection of Disease by Two-Dimensional Echocardiography](https://arxiv.org/abs/1706.07342) - 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](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138894/) - 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](https://arxiv.org/abs/1808.07168) - Nathalie-Sofia Tomov, Stanimire Tomov (2018)

* [Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging](https://www.ncbi.nlm.nih.gov/pubmed/30060039) - 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](https://link.springer.com/article/10.1007/s12410-019-9480-x) - Luis Eduardo Juarez-Orozco, Octavio Martinez-Manzanera, Andrea Ennio Storti, Juhani Knuuti (2019)

* [Water–fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network](https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.26658) - James W. Goldfarb, Jason Craft, J. Jane Cao (2019)

* [Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease](https://www.nature.com/articles/s41746-018-0065-x) - Ali Madani, Jia Rui Ong, Anshul Tibrewal, Mohammad R. K. Mofrad (2018)

#### Arrhythmias

* [A Neural Network System for Detection of Atrial Fibrillation in Ambulatory Electrocardiograms](https://www.researchgate.net/publication/15210772_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](https://www.ncbi.nlm.nih.gov/pubmed/11258582) - H. Al-Nashash (2000)

* [Classification of arrhythmia using machine learning techniques](https://pdfs.semanticscholar.org/b37e/d3ee8dab4e8609223eb9155ae3a8cdf612ce.pdf) - Thara Soman, Patrick O. Bobbie (2004)

* [Neural network based arrhythmia classification using Heart Rate Variability signal](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.470.9813&rep=rep1&type=pdf) - Babak Mohammadzadeh Asl, Seyed Kamaledin Setarehdan (2006)

* [Artificial Neural Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG Signal Data](https://pdfs.semanticscholar.org/d0d3/19f042d48abd7533f5f9d95aeda8da711a38.pdf) - Shivajirao M. Jadhav, Sanjay L. Nalbalwar, Ashok A. Ghatol (2012)

* [Automated detection & classification of arrhythmias](http://cs229.stanford.edu/proj2014/Richard%20Tang,%20Saurabh%20Vyas,%20Automated%20Detection%20and%20Classification%20of%20Cardiac%20Arrhythmias.pdf) - Richard Tang, Saurabh Vyas (2014)

* [Cardiac Dysrhythmia Detection with GPU-Accelerated Neural Networks](http://cs229.stanford.edu/proj2014/Albert%20Haque,%20Cardiac%20Dysrhythmia%20Detection%20with%20GPU-Accelerated%20Neural%20Networks.pdf) - [[Code](https://github.com/ahaque/arrhythmia-nn)] - Albert Haque (2014)

* [Deep learning approach for active classification of electrocardiogram signals](https://www.sciencedirect.com/science/article/pii/S0020025516300184) - 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](https://www.researchgate.net/publication/311448170_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](https://ieeexplore.ieee.org/abstract/document/8228452) - Hilmy Assodiky, Iwan Syarif, Tessy Badriyah (2017)

* [Cardiologist-Level Arrhythmia Detection With Convolutional Neural Networks](https://stanfordmlgroup.github.io/projects/ecg/) - 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](https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2017.0821) - Aurore Lyon, Ana Minchole, Juan Pablo Martinez, Pablo Laguna, Blanca Rodriguez (2017)

* [Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records](https://arxiv.org/abs/1711.03892) - Tomás Teijeiro, Constantino A. García, Daniel Castro, Paulo Félix (2017)

* [Comparing Feature Based Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG](https://www.researchgate.net/publication/324049188_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](https://www.mdpi.com/1424-8220/17/11/2445/pdf) - 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](https://ieeexplore.ieee.org/document/8260688) - Halil Ibrahim, Nese Usta, Musa Yildiz (2017)

* [Deep learning algorithm for arrhythmia detection](https://ieeexplore.ieee.org/document/8228452) - Hilmy Assodiky, Iwan Syarif, Tessy Badriyah (2017)

* [Cardiac arrhythmia detection using deep learning](https://www.researchgate.net/publication/321812830_Cardiac_arrhythmia_detection_using_deep_learning) - Ali Isina, Selen Ozdalilib (2017)

* [Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants](https://www.hindawi.com/journals/cmmm/2018/7310496/) - Anam Mustaqeem, Syed Muhammad Anwar, Muahammad Majid (2018)

* [ECG arrhythmia classification using a 2-D convolutional neural network](https://arxiv.org/abs/1804.06812) - Tae Joon Jun, Hoang Minh Nguyen, Daeyoun Kang, Dohyeun Kim, Daeyoung Kim, Young-Hak Kim (2018)

* [Arrhythmia Detection Using Deep Convolutional Neural Network With Long Duration ECG Signals](https://www.researchgate.net/publication/327602644_Arrhythmia_Detection_Using_Deep_Convolutional_Neural_Network_With_Long_Duration_ECG_Signals) - Özal yıldırım, Paweł Pławiak, Ru San Tan, U Rajendra Acharya (2018)

* [Personalizing a Generic ECG Heartbeat Classification for Arrhythmia Detection: A Deep Learning Approach](https://www.semanticscholar.org/paper/Personalizing-a-Generic-ECG-Heartbeat-for-A-Deep-Wu-Chang/52ad25136ce7f263c3ae3e5f7b544c22beab2c9f) - Meng-Hsi Wu, Emily J. Chang, Tzu-Hsuan Chu (2018)

* [Inter-Patient ECG Classification with Convolutional and Recurrent Neural Networks](https://arxiv.org/pdf/1810.04121.pdf)  Li Guo, Gavin Sim, Bogdan Matuszewski (2018)

* [A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms](https://iopscience.iop.org/article/10.1088/1361-6579/aae304) - Philipp Sodmann, Marcus Vollmer, Neetika Nath, Lars Kaderali (2018)

* [Automated ECG Classification using Dual Heartbeat Coupling based on Convolutional Neural Network](https://www.researchgate.net/publication/325025844_Automated_ECG_Classification_using_Dual_Heartbeat_Coupling_based_on_Convolutional_Neural_Network) - Xiaolong Zhai, Chung Tin (2018)

* [Classification of short single lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost](https://www.researchgate.net/publication/327461887_Classification_of_short_single_lead_electrocardiograms_ECGs_for_atrial_fibrillation_detection_using_piecewise_linear_spline_and_XGBoost) - Yao Chen, Xiao Wang, Yonghan Jung, Mohammad Adibuzzaman (2018)

* [Inter-Patient ECG Classification Using Deep Convolutional Neural Networks](https://ieeexplore.ieee.org/document/8491848) - Janne Takalo-Mattila, Jussi Kiljander, Juha-Pekka Soininen (2018)

* [ECG Signal Classification for the Detection of Cardiac Arrhythmias Using a Convolutional Recurrent Neural Network](https://www.researchgate.net/publication/326941827_ECG_Signal_Classification_for_the_Detection_of_Cardiac_Arrhythmias_Using_a_Convolutional_Recurrent_Neural_Network) - Zhaohan Xiong, Martyn P Nash, Elizabeth Cheng, Jichao Zhao (2018)

* [An automatic cardiac arrhythmia classification system with wearable electrocardiogram](https://www.researchgate.net/publication/323330302_An_automatic_cardiac_arrhythmia_classification_system_with_wearable_electrocardiogram) - Yufa Xia, Zhifan Gao, Huailing Zhang, Shuo Li (2018)

* [Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6027502/) - Shalin Savalia, Vahid Emamian (2018)

* [ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features](https://arxiv.org/abs/1812.04693) - Milad Salem, Shayan Taheri, Jiann Shiun-Yuan (2018)

* [Arrhythmia Classification in Multi-Channel ECG Signals Using Deep Neural Networks](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-80.pdf) - Kyungna Kim (2018)

* [Cardiac Arrhythmia Classification Using Machine Learning Techniques](https://link.springer.com/chapter/10.1007/978-981-13-1642-5_42) - 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](https://iopscience.iop.org/article/10.1088/1361-6579/aad5bd) - 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](https://iopscience.iop.org/article/10.1088/1361-6579/aad9ee) - Filip Plesinger, Petr Nejedly, Ivo Viscor, Josef Halamek, Pavel Jurak (2018)

* [Classification of ECG Arrhythmia using Recurrent Neural Networks](https://www.sciencedirect.com/science/article/pii/S1877050918307774) - Shraddha Singh, Saroj Kumar Pandey, Urja Pawar, Rekh Ram Janghel (2018)

* [A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification](https://www.researchgate.net/publication/324056729_A_novel_wavelet_sequence_based_on_deep_bidirectional_LSTM_network_model_for_ECG_signal_classification) - Özal yıldırım (2018)

* [Efficient wavelet families for ECG classification using neural classifiers](https://www.sciencedirect.com/science/article/pii/S1877050918307865) - Ritu Singh, Rajesh Mehta, Navin Rajpal (2018)

* [Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory Networks](https://arxiv.org/abs/1801.10033) - Philip Warrick, Masun Nabhan Homsi (2018)

* [Artificial Intelligence in Cardiac Arrhythmia Classification](https://www.semanticscholar.org/paper/Artificial-Intelligence-in-Cardiac-Arrhythmia-Chen-Li/9e529054f02107d00cd11ab34df7522f3545dee6) - Siteng Chen, An-Jui. Li, Janet Roveda (2018)

* [Arrhythmia Classification of ECG Signals Using Hybrid Features](https://www.hindawi.com/journals/cmmm/2018/1380348/) - Syed Muhammad Anwar, Maheen Gul, Muhammad Majid, Majdi Alnowami (2018)

* [Detection of Atrial Fibrillation from RR Intervals and PQRST Morphology using a Neural Network Ensemble](https://www.ncbi.nlm.nih.gov/pubmed/30441703) - Heba Khamis, Jiayu Chen, J. Stephen Redmond, Nigel Lovell, Nigel Lovell (2018)

* [Computer-Aided Arrhythmia Diagnosis by Learning ECG Signal](https://arxiv.org/pdf/1810.04123.pdf) - Sai Manoj, Matthias Wess (2018)

* [A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using varying length single lead electrocardiogram](https://www.researchgate.net/publication/322703262_A_robust_deep_convolutional_neural_network_for_the_classification_of_abnormal_cardiac_rhythm_using_varying_length_single_lead_electrocardiogram) - Rishikesan Kamaleswaran, Ruhi Mahajan, Oguz Akbilgic (2018)

* [ECG Heartbeat Classification: A Deep Transferable Representation](https://arxiv.org/abs/1805.00794) - Mohammad Kachuee, Shayan Fazeli, Majid Sarrafzadeh (2018)

* [An Attention-Based CNN for ECG Classification](https://link.springer.com/chapter/10.1007/978-3-030-17795-9_49) - Alexander Kuvaev, Roman Khudorozhkov (2019)

* [ECG Classification Using Artificial Neural Networks](https://www.researchgate.net/publication/333771943_ECG_Classification_Using_Artificial_Neural_Networks) - F. A. Rivera Sánchez, J. A. González Cervera (2019)

* [On Arrhythmia Detection by Deep Learning and Multidimensional Representation](https://arxiv.org/abs/1904.00138) - K.S. Rajput, S. Wibowo, C. Hao, M. Majmudar (2019)

* [ECG Arrhythmias Detection Using Auxiliary Classifier Generative Adversarial Network and Residual Network](https://doi.org/10.1109/ACCESS.2019.2930882) - Pu Wang, Borui Hou, Siyu Shao, Ruqiang Yan (2019)

* [Graphical analysis of the progression of atrial arrhythmia through an ensemble of Generative Adversarial Network Discriminators](https://dx.doi.org/10.2991/eusflat-19.2019.78) - Graphical analysis of the progression of atrial arrhythmia through an ensemble of Generative Adversarial Network Discriminators (2019)

* [Assessment of Electrocardiogram Rhythms by GoogLeNet Deep Neural Network Architecture](https://www.hindawi.com/journals/jhe/2019/2826901/) - Jeong-Hwan Kim, Seung-Yeon Seo, Chul-Gyu Song, Kyeong-Seop Kim (2019)

* [Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network](https://www.sciencedirect.com/science/article/pii/S1566253518307632) - Qihang Yao, Ruxin Wang, Xiaomao Fan, Jikui Liu, Ye Li (2019)

* [ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System](https://arxiv.org/pdf/1901.03808.pdf) - Huangxun Chen, Chenyu Huang, Qianyi Huang, Qian Zhang, Wei Wang (2019)

* [Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network](https://stanfordmlgroup.github.io/projects/ecg2/) - Awni Y. Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H. Tison, Codie Bourn, Mintu P. Turakhia, Andrew Y. Ng (2019)

* [Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection](https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12432) - Yuwei Zhang, Yuan Zhang, Benny Lo, Wenyao Xu (2019)

* [Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings](https://iopscience.iop.org/article/10.1088/1361-6579/ab15a2) - Shenda Hong, Yuxi Zhou, Meng Wu, Junyuan Shang, Qingyun Wang, Hongyan Li, Junqing Xie (2019)

* [ECG Signal Classification Algorithm Based on Fusion Features](https://www.researchgate.net/publication/332690450_ECG_Signal_Classification_Algorithm_Based_on_Fusion_Features) - Ma Guanglong, Wang Xiangqing, Yu Junsheng (2019)

* [Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals](https://link.springer.com/article/10.1007/s00521-018-03980-2) - Paweł Pławiak, U. Rajendra Acharya (2019)

* [Inter and Intra Patient ECG Heartbeat Classification For Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach](https://engrxiv.org/uedzx/) - Sajad Mousavi, Fatemeh Afghah, U. Rajendra Acharya (2019)

* [Deep Learning of Arrhythmia Analysis Based on Convolutional Neural Network](http://www.ijbem.org/volume21/number1/48-58.pdf) - Anake Pomprapa , Waqar Ahmeda, André Stollenwerk , Stefan Kowalewskib, and Steffen Leonhardt (2019)

* [Diagnosing Abnormal Electrocardiogram (ECG) via Deep Learning](https://www.intechopen.com/online-first/diagnosing-abnormal-electrocardiogram-ecg-via-deep-learning) - Xin Gao (2019)

* [Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning](https://www.mdpi.com/1999-4893/12/2/32/htm) - Alessandro Scirè, Fabrizio Tropeano, Aris Anagnostopoulos, Ioannis Chatzigiannakis (2019)

* [On Arrhythmia Detection by Deep Learning and Multidimensional Representation](https://arxiv.org/abs/1904.00138) - K.S. Rajput, S. Wibowo, C. Hao, M. Majmudar (2019)

* [Adversarial Examples for Electrocardiograms](https://evidation.com/research/adversarial-examples-for-ecg/) - X. Han, Y. Hu, L. Foschini, L. Jankelson, R. Ranganath (2019)

* [A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8721643) - Hao Dang, Muyi Sun, Guanhong Zhang, Xingqun Qi, Xiaoguang Zhou, Qing Chang (2019)

* [Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6334203/) - Chris D. Cantwell, Yumnah Mohamied, Konstantinos N. Tzortzis, Stef Garasto, Charles Houston, Rasheda A. Chowdhury, Fu Siong Ng, Anil A. Bharath, Nicholas S. Peters (2019)

* [A new approach for arrhythmia classification using deep coded features and LSTM networks](https://www.sciencedirect.com/science/article/pii/S0169260718314329) - Ozal Yildirim, Ulas Baran Baloglua, Ru-San Tan, Edward J.Ciaccio, U. Rajendra Acharya (2019)

* [Heartbeat Anomaly Detection using Adversarial Oversampling](https://arxiv.org/abs/1901.09972) - Jefferson L. P. Lima, David Macêdo, Cleber Zanchettin (2019)

* [Proposing feature engineering method based on deep learning and K-NNs for ECG beat classification and arrhythmia detection](https://doi.org/10.1007/s13246-019-00814-w) - Toktam Khatibi, Nooshin Rabinezhadsadatmahaleh (2019)

* [Computer-Aided Arrhythmia Diagnosis with Bio-signal Processing: A Survey of Trends and Techniques](https://www.researchgate.net/publication/331586318_Computer-Aided_Arrhythmia_Diagnosis_with_Bio-signal_Processing_A_Survey_of_Trends_and_Techniques) - Sai Manoj, Axel Jantsch, Axel Jantsch, Muhammad Shafique, Muhammad Shafique (2019)

##### Realtime analysis

* [A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks](https://pdfs.semanticscholar.org/fe24/c4b5a37190ba6cb3ed4db6ab973262efe186.pdf) - Sheng Hu, Hongxing Wei, Youdong Chen, Jindong Tan (2012)

* [Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks](https://ieeexplore.ieee.org/document/7202837) - Serkan Kiranyaz, Turker Ince, Moncef Gabbouj (2015)

* [Real-Time Detection of Atrial Fibrillation from Short Time Single Lead ECG Traces Using Recurrent Neural Networks](https://link.springer.com/chapter/10.1007/978-3-319-68385-0_18) - V. G. Sujadevi, K. P. Soman, R. Vinayakumar (2017)

* [A deep learning approach for real-time detection of atrial fibrillation](https://www.sciencedirect.com/science/article/pii/S0957417418305190) - Rasmus S. Andersen, Abdolrahman Peimankar, Sadasivan Puthusserypady (2018)

* [A Real-time Arrhythmia Heartbeats Classification Algorithm using Parallel Delta Modulations and Rotated Linear-Kernel Support Vector Machines](https://doi.org/10.1109/TBME.2019.2926104)

##### Signal optimization

* [Noise Detection in Electrocardiography Signal for Robust Heart Rate Variability Analysis: A Deep Learning Approach](https://www.researchgate.net/publication/328986749_Noise_Detection_in_Electrocardiography_Signal_for_Robust_Heart_Rate_Variability_Analysis_A_Deep_Learning_Approach) - Sardar Ansari, Jonathan Gryak, Kayvan Najarian, Kayvan Najarian (2018)

* [PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification](https://web.archive.org/web/20190723215848/http://www.kiraradinsky.com/files/pgans-personalized-generative.pdf) - Tomer Golany, Kira Radinsky (2019)

#### Artery disease

##### Atherosclerosis

* [Early-stage atherosclerosis detection using deep learning over carotid ultrasound images](https://www.sciencedirect.com/science/article/pii/S1568494616304574) - Rosa-María Menchón-Lara, José-Luis Sancho-Gómez, Andrés Bueno-Crespo (2016)

* [A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5293622/) - Karim Lekadir, Alfiia Galimzianova, Àngels Betriu, Maria del Mar Vila, Laura Igual, Daniel L. Rubin, Elvira Fernández, Petia Radeva, Sandy Napel (2016)

* [Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification](https://arxiv.org/abs/1805.06223) - Nils Gessert, Markus Heyder, Sarah Latus, David M. Leistner, Youssef S. Abdelwahed, Matthias Lutz, Alexander Schlaefer (2018)

* [A deep learning approach to classify atherosclerosis using intracoronary optical coherence tomography](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10950/109500N/A-deep-learning-approach-to-classify-atherosclerosis-using-intracoronary-optical/10.1117/12.2513078.short) - Lambros Athanasiou, Max Olender, José M. de la Torre Hernandez, Eyal Ben-Assa, Elazer Edelman (2019)

* [Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning](https://www.journalofcardiovascularct.com/article/S1934-5925(18)30615-4/fulltext) - Márton Kolossváry, Carlo N. De Cecco, Gudrun Feuchtner, Pál Maurovich-Horvat (2019)

* [A Machine Learning-Based Method for Intracoronary OCT Segmentation and Vulnerable Coronary Plaque Cap Thickness Quantification](https://www.researchgate.net/publication/322824245_A_Machine_Learning-Based_Method_for_Intracoronary_OCT_Segmentation_and_Vulnerable_Coronary_Plaque_Cap_Thickness_Quantification) - Xiaoya Guo, Dalin Tang, David Molony, Chun Yang, Habib Samady, Jie Zheng, Gary S. Mintz, Akiko Maehara, Liang Wang, Xuan Pei, Zhi-Yong Li, Genshan Ma, Don P. Giddens (2019)

* [Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk](https://arxiv.org/abs/1809.04663) - Stephen Pfohl, Ben Marafino, Adrien Coulet, Fatima Rodriguez, Latha Palaniappan, Nigam H. Shah (2019)

##### Carotid

* [Neural network analysis of internal carotid arterial Doppler signals: predictions of stenosis and occlusion](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.115.5256&rep=rep1&type=pdf) - Elif Derya Übeyli, İnan Güler (2003)

* [Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals](https://www.ncbi.nlm.nih.gov/pubmed/16124990) - Elif Derya Übeyli, İnan Güler (2005)

* [Carotid Artery Characterization in Ultrasound Imaging using Machine Learning Techniques](http://iscb2017.info/uploadedFiles/ISCB2017.y23bw/fileManager/ISCB38%20SY3%20MARIA%20DEL%20MAR%20VILA.pdf) - Maria del Mar Vila (2017)

* [Atherosclerotic carotid plaques on panoramic imaging: an automatic detection using deep learning with small dataset](https://arxiv.org/pdf/1808.08093.pdf) - Lazar Kats, Marilena Vered, Ayelet Zlotogorski-Hurvitz, Itai Harpaz (2018)

##### Coronary

* [Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis](https://www.researchgate.net/publication/321304894_Deep_learning_analysis_of_the_myocardium_in_coronary_CT_angiography_for_identification_of_patients_with_functionally_significant_coronary_artery_stenosis) - Majd Zreik, Nikolas Lessmann, Robbert van Hamersvelt, Jelmer Wolterink, Michiel Voskuil, Max A. Viergever, Tim Leiner, Ivana Išgum (2017)

* [Deep Neural Networks for ECG-free Cardiac Phase and End-Diastolic Frame Detection on Coronary Angiographies](https://arxiv.org/abs/1811.02797) - Costin Ciusdel, Alexandru Turcea, Andrei Puiu, Lucian Itu, Lucian Calmac, Emma Weiss, Cornelia Margineanu, Elisabeta Badila, Martin Berger, Thomas Redel, Tiziano Passerini, Mehmet Gulsun, Puneet Sharma (2018)

* [Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography](https://arxiv.org/abs/1806.09748) - Eunhee Kang, Hyun Jung Koo, Dong Hyun Yang, Joon Bum Seo, Jong Chul Ye (2018)

* [Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images](https://www.researchgate.net/publication/329374065_Deep_neural_networks_for_A-line-based_plaque_classification_in_coronary_intravascular_optical_coherence_tomography_images) - Chaitanya Kolluru, David Prabhu, Yazan Gharaibeh, Hiram Bezerra, Giulio Guagliumi, David Wilson (2018)

* [Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks](https://arxiv.org/abs/1807.10597) - Benjamin Au, Uri Shaham, Sanket Dhruva, Georgios Bouras, Ecaterina Cristea, Alexandra Lansky MD, Andreas Coppi, Fred Warner, Shu-Xia Li, Harlan Krumholz (2018)

* [Coronary heart disease diagnosis by artificial neural networks including aortic pulse wave velocity index and clinical parameters](https://journals.lww.com/jhypertension/Abstract/2019/08000/Coronary_heart_disease_diagnosis_by_artificial.19.aspx) - Alexandre Vallée, Alexandre Cinaud, Vincent Blachier, Hélène Lelong, Michel Safar, Jacques Blacher (2019)

##### Peripheral

* [The use of machine learning for the identification of peripheral artery disease and future mortality risk](https://www.jvascsurg.org/article/S0741-5214(16)30166-5/fulltext) - Elsie Gyang Ross, Nigam H. Shah, Ronald L. Dalman, Kevin T. Nead, John P. Cooke, Nicholas J. Leeper, Nicholas J. Leeper, Nicholas J. Leeper (2016)

#### Congenital disease

* [Ontology based congenital heart disease diagnosis using neural networks](https://pdfs.semanticscholar.org/c248/1033deed024fd60e54e19b7616cec85e39d2.pdf) - A. Nirmala, Abinaya Sambath Kumar (2015)

* [Predicting congenital heart defects: A comparison of three data mining methods](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177811) - Yanhong Luo, Zhi Li, Husheng Guo, Hongyan Cao, Chunying Song, Xingping Guo, Yanbo Zhang (2017)

* [An artificial neural network prediction model of congenital heart disease based on risk factors](https://journals.lww.com/md-journal/FullText/2017/02100/An_artificial_neural_network_prediction_model_of.50.aspx) - Huixia Li, Miyang Luo, Jianfei Zheng, Jiayou Luo, Rong Zeng, Na Feng, Qiyun Du, Junqun Fang (2017)

* [Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5260631/) - Franklin Pereira, Alejandra Bueno, Andrea Rodriguez, Douglas Perrin, Gerald Marx, Michael Cardinale, Ivan Salgo, Pedro del Nidob (2017)

* [Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease](https://arxiv.org/abs/1704.03669) - 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](https://arxiv.org/pdf/1809.06993.pdf) - Rima Arnaout, Lara Curran, Erin Chinn, Yili Zhao, Anita Moon-Grady (2018)

* [Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning](https://www.ncbi.nlm.nih.gov/pubmed/29538684) - Manar D Samad, Gregory J Wehner, Mohammad R Arbabshirani, Linyuan Jing, Andrew J Powell, Tal Geva, Christopher M Haggerty, Brandon K Fornwalt (2018)

* [Real‐time cardiovascular MR with spatio‐temporal artifact suppression using deep learning–proof of concept in congenital heart disease](https://onlinelibrary.wiley.com/doi/10.1002/mrm.27480) - Andreas Hauptmann, Simon Arridge, Felix Lucka, Vivek Muthurangu, Jennifer A. Steeden (2018)

#### Heart failure

* [A Cognitive Machine Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis from Restrictive Cardiomyopathy](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5321667/) - Partho P. Sengupta, Yen-Min Huang, Manish Bansal, Ali Ashrafi, Matt Fisher, Khader Shameer, Walt Gall, Joel T Dudley (2017)

* [A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882098/) - Jeffrey J. Nirschl, Andrew Janowczyk, Eliot G. Peyster, Renee Frank, Kenneth B. Margulies, Michael D. Feldman, Anant Madabhushi (2018)

#### Myocardial infarction

* [Neural Network Analysis of Serial Cardiac Enzyme Data A Clinical Application of Artificial Machine Intelligence](https://academic.oup.com/ajcp/article-abstract/96/1/134/1828762) - James W. Furlong, Milton E. Dupuy, James A. Heinsimer (1991)

* [Automated Risk Identification of Myocardial Infarction Using Relative Frequency Band Coefficient (RFBC) Features from ECG](https://www.researchgate.net/publication/51177719_Automated_Risk_Identification_of_Myocardial_Infarction_Using_Relative_Frequency_Band_Coefficient_RFBC_Features_from_ECG) - Bakul Gohel, Uma Shanker Tiwary (2010)

* [Myocardial infarction detection and classification — A new multi-scale deep feature learning approach](https://ieeexplore.ieee.org/document/7868568) - J. F. Wu, Y. L. Bao, S. C. Chan, H. C. Wu, L. Zhang, X. G. Wei (2016)

* [Classification of Myocardial Infarction Using Multi Resolution Wavelet Analysis of ECG](https://www.sciencedirect.com/science/article/pii/S2212017316302857) - R. S. Remya, K. P. Indiradevi, K. K. Anish Babu (2016)

* [Application of Deep Convolutional Neural Network for Automated Detection of Myocardial Infarction Using ECG Signals](https://www.researchgate.net/publication/317821702_Application_of_Deep_Convolutional_Neural_Network_for_Automated_Detection_of_Myocardial_Infarction_Using_ECG_Signals) - U Rajendra Acharya, Hamido Fujita, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam (2017)

* [Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices](https://www.sciencedirect.com/science/article/pii/S2352914818301333) - Hin Wai Lui, King Lau Chow (2018)

* [Multi-Channel Lightweight Convolution Neural Network for Anterior Myocardial Infarction Detection](https://www.researchgate.net/publication/329480041_Multi-Channel_Lightweight_Convolution_Neural_Network_for_Anterior_Myocardial_Infarction_Detection) - Yufei Chen, Huihui Chen, Ziyang He, Cong Yang, Yangjie Cao (2018)

* [A Simple and Effective Method for Detecting Myocardial Infarction Based on Deep Convolutional Neural Network](https://www.researchgate.net/publication/327375583_A_Simple_and_Effective_Method_for_Detecting_Myocardial_Infarction_Based_on_Deep_Convolutional_Neural_Network) - Na Liu, Ludi Wang, Qing Chang, Ying Xing, Xiaoguang Zhou (2018)

* [Detecting and interpreting myocardial infarction using fully convolutional neural networks](https://arxiv.org/pdf/1806.07385.pdf) - Nils Strodthoff, Claas Strodthoff (2019)

* [Automated Detection and Localization of Myocardial Infarction with Staked Sparse Autoencoder and TreeBagger](https://www.researchgate.net/publication/333413230_Automated_Detection_and_Localization_of_Myocardial_Infarction_with_Staked_Sparse_Autoencoder_and_TreeBagger) - Jieshuo Zhang, Feng Lin, Peng Xiong, Haiman Du, Hong Zhang, Ming Liu, Zengguang Hou, Xiuling Liub (2019)

* [A Wavelet-Based ECG Delineation and Automated Diagnosis of Myocardial Infarction in PTB Database](https://eudl.eu/pdf/10.4108/eai.24-4-2019.2284216) - Rachid Haddadi, Elhassane Abdelmounim, Mustapha El Hanine, Abdelaziz Belaguid (2019)

* [A Novel Deep Transfer Learning Method for Detection of Myocardial Infarction](https://arxiv.org/abs/1906.09358) - Mohamed Adel Hammad (2019)

* [Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features](https://www.sciencedirect.com/science/article/pii/S0169260719301531) - Chuang Han, Li Shi (2019)

* [Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network](https://www.researchgate.net/publication/332974912_Myocardial_Infarction_Classification_Based_on_Convolutional_Neural_Network_and_Recurrent_Neural_Network) - Kai Feng, Xitian Pi, Hongying Liu, Kai Sun (2019)

* [Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI](https://pubs.rsna.org/doi/10.1148/radiol.2019182304) - Nan Zhang, Guang Yang, Zhifan Gao, Chenchu Xu, Yanping Zhang, Rui Shi, Jennifer Keegan, Lei Xu , Heye Zhang, Zhanming Fan, David Firmin (2019)

* [Classification of Myocardial Infarction with Multi-Lead ECG Signals and Deep CNN](https://www.researchgate.net/publication/331035341_Classification_of_Myocardial_Infarction_with_Multi-Lead_ECG_Signals_and_Deep_CNN) - Ulas Baran Baloglu, Muhammed Talo, Özal yıldırım, Ru San Tan, U Rajendra Acharya (2019)

### Prediction

* [Noninvasive diagnosis of coronary artery disease using a neural network algorithm](https://www.ncbi.nlm.nih.gov/pubmed/1515514) - Metin Akay (1992)

* [Machine Learning Data Imputation and Classification in a Multicohort Hypertension Clinical Study](https://journals.sagepub.com/doi/full/10.4137/BBI.S29473) - William Seffens, Chad Evans, Taylor Minority Health-Grid Network And Herman (2016)

* [Artificial Intelligence in Mitral Valve Analysis](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408514/) - 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](https://academic.oup.com/eurheartj/article/38/suppl_1/ehx502.P1089/4088432) - L.E. Juarez-Orozco R.J.J. Knol C.A. Sanchez-Catasus F.M. Van Der Zant J. Knuuti (2017)

* [Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis](https://www.ahajournals.org/doi/full/10.1161/CIRCRESAHA.117.311312) - 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)

* [DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction](https://arxiv.org/pdf/1802.02511.pdf) - 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](https://academic.oup.com/ehjcimaging/article/20/Supplement_3/jez145.005/5512889) - L E Juarez-Orozco O Martinez-Manzanera F M Van Der Zant R J J Knol J Knuuti (2019)

#### Acute coronary syndrome

* [A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records](https://ieeexplore.ieee.org/document/7990180) - Zhengxing Huang, Wei Dong, Huilong Duan, Jiquan Liu (2017)

#### Cardiopulmonary arrest

* [An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6064911/) - Joon‐myoung Kwon, Youngnam Lee, Yeha Lee, Seungwoo Lee, Jinsik Park (2018)

#### Hypertension

* [Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area](https://www.nature.com/articles/hr201073) - 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](https://globaljournals.org/GJCST_Volume11/5-Pre-Diagnosis-of-Hypertension-Using-Artificial-Neural-Network.pdf) - B. Sumathi, A. Santhakumaran (2011)

* [Anthropometric Predictors and Artificial Neural Networks in the diagnosis of Hypertension](https://www.researchgate.net/publication/300338220_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](https://arxiv.org/pdf/1907.00089.pdf) - Ramin Mohammadi, Sarthak Jain, Stephen Agboola, Ramya Palacholla, Sagar Kamarthi, Byron C. Wallace (2019)

#### Sudden cardiac death

* [A novel multi-class approach for early-stage prediction of sudden cardiac death](https://www.sciencedirect.com/science/article/abs/pii/S0208521619300191) - Reeta Devi, Hitender Kumar Tyagi, Dinesh Kumar (2019)

#### Surgery

* [Predicting Mortality after Coronary Artery Bypass Surgery: What Do Artificial Neural Networks Learn?](https://www.researchgate.net/publication/297987046_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](https://www.ncbi.nlm.nih.gov/pubmed/11120637) - Dan A. Buzatu, Kim K. Taylor, Daniel C. Peret, Nicholas P. Lang (2001)

* [Weight-elimination Neural Networks Applied to Coronary Surgery Mortality Prediction](http://www.sce.carleton.ca/faculty/frize/MIRG_2001/TRANS-JOUR-CME2.pdf) - Colleen M. Ennett, Monique Frize (2002)

* [Risk factor identification and mortality prediction in cardiac surgery using artificial neural networks](https://www.jtcvs.org/article/S0022-5223(06)00124-3/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](https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2044.2008.05478.x) - S. Y. Peng, S. K. Peng (2008)

* [Artificial Neural Networks Prognostic Evaluation of Post-Surgery Complications in Patients Underwent to Coronary Artery Bypass Graft Surgery](https://www.researchgate.net/publication/221226545_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)

* [Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0145395) - 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](https://europepmc.org/articles/pmc4548023) - 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](https://e-hir.org/DOIx.php?id=10.4258/hir.2018.24.2.109) - 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](http://www.onlinejacc.org/content/71/11_Supplement/A1357) - Rashmee Shah, Yijun Shao, Kristina Doing-Harris, Charlene Weir, Yan Cheng, Bruce Bray, Qing Zeng (2018)

* [Machine learning techniques in cardiac risk assessment](https://pdfs.semanticscholar.org/a250/3dcd968261a15129718e1eeed73c55cf04ae.pdf) - Elif Kartal, Mehmet Erdal Balaban (2018)

#### Revascularization

* [Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210103) - Shinichi Goto, Mai Kimura, Yoshinori Katsumata, Shinya Goto, Takashi Kamatani, Genki Ichihara, Seien Ko, Junichi Sasaki, Keiichi Fukuda, Motoaki Sano (2019)

### Prognostics

* [Machine learning improves the long-term prognostic value of sequential cardiac PET/CT](https://academic.oup.com/eurheartj/article-abstract/39/suppl_1/ehy563.3003/5080092) - 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](https://arxiv.org/abs/1811.10553) - 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)

* [Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study](https://www.researchgate.net/publication/331909159_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 Prediction of Response to Cardiac Resynchronization Therapy](https://www.ahajournals.org/doi/10.1161/CIRCEP.119.007316) - 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)

#### Acute coronary syndrome

* [Machine Learning Improves Risk Stratification After Acute Coronary Syndrome](https://www.nature.com/articles/s41598-017-12951-x) - 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](https://tandfonline.com/doi/abs/10.1080/07853890.2019.1596302?af=R&journalCode=iann20) - 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)

* [Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers](https://www.hindawi.com/journals/dm/2019/9056402/) - Konrad Pieszko, Jarosław Hiczkiewicz, Paweł Budzianowski, Jan Budzianowski, Janusz Rzeźniczak, Karolina Pieszko, Paweł Burchardt (2019)

#### Artery disease

* [Use of Machine Learning to Accurately Predict Adverse Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5079774/) - Elsie Gyang Ross, Nigam Shah, Ronald Dalman, Kevin Nead, John Cooke, Nicholas J. Leeper (2017)

#### Cardiac amyloidosis

* [Machine Learning Predicts Mortality Better Than Biomarker Staging in Wild-type Cardiac Amyloidosis](https://www.ahajournals.org/doi/abs/10.1161/circ.136.suppl_1.19035) - Avinainder Singh, Hallie I Geller, Rodney H Falk (2018)

#### Congenital disease

* [Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10,019 patients](https://www.ncbi.nlm.nih.gov/pubmed/30689812) - 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)

#### Heart failure

* [Artificial Intelligence Improves Accuracy of Heart Failure Readmission Risk Predictions](https://www.healthcatalyst.com/success_stories/machine-learning-in-heart-failure-multicare) - HealthCatalyst, Pulse Heart Institute, MultiCare (2018)

#### Surgery

* [Use of a neural network as a predictive instrument for length of stay in the intensive care unit following cardiac surgery](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2248140/) - J. V. Tu, M. R. Guerriere (1992)

##### Coronary artery bypass

* [Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery](https://arxiv.org/abs/1812.00596) - Ramesh B. Manyam, Yanqing Zhang, William B. Keeling, Jose Binongo, Michael Kayatta, Seth Carter (2018)

### Simulation

* [ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy Gradient](https://doi.org/10.1109/ACCESS.2019.2950383) - Fei Ye, Fei Zhu, Yuchen Fu, Bairong Shen (2019)

* [Synthesis of Realistic ECG using Generative Adversarial Networks](https://arxiv.org/abs/1909.09150) Anne Marie Delaney, Eoin Brophy, Tomas E. Ward - (2019)

### Identification

#### Devices

* [Cardiac Rhythm Device Identification Using Neural Networks](http://electrophysiology.onlinejacc.org/content/5/5/576) - 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)

#### Individuals

* [Personal Identification by Convolutional Neural Network with ECG Signal](https://ieeexplore.ieee.org/document/8539632) - Jianbo Xu, Tianhui Li, Ying Chen, Wenxi Chen (2018)

* [Finger ECG based Two-phase Authentication Using 1D Convolutional Neural Networks](https://www.researchgate.net/publication/328991830_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](https://www.researchgate.net/publication/332662657_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](https://www.sciencedirect.com/science/article/abs/pii/S0167865519302004) - 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](https://www.hindawi.com/journals/wcmc/2019/6458719/) - Min-Gu Kim, Hoon Ko, Sung Bum Pan (2019)

##### Privacy

* [Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing](https://www.ahajournals.org/doi/10.1161/CIRCOUTCOMES.118.005122) - Brett K. Beaulieu-Jones , Zhiwei Steven Wu , Chris Williams , Ran Lee , Sanjeev P. Bhavnani , James Brian Byrd , Casey S. Greene (2019)

### Treatment

#### Acute coronary syndrome

* [Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome](https://link.springer.com/article/10.1186/s12911-018-0730-7) - Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang (2019)

#### Pharmacology

##### Efficacy

* [Towards Identifying of Effective Personalized Antihypertensive Treatment Rules from Electronic Health Records Data Using Classification Methods: Initial Model](https://www.sciencedirect.com/science/article/pii/S1877050917323128?via%3Dihub) - Anna Semakova, Nadezhda Zvartau, Klavdiya Bochenina, Aleksandra Konradi (2017)

* [Machine learning of big data in gaining insight into successful treatment of hypertension](https://bpspubs.onlinelibrary.wiley.com/doi/full/10.1002/prp2.396) - Gideon Koren, Galia Nordon, Kira Radinsky, Varda Shalev (2018)

##### Drug delivery

* [Artificial Neural Network Modeling of Sustained Antihypertensive Drug Delivery using Polyelectrolyte Complex based on Carboxymethyl-kappa-carrageenan and Chitosan as Prospective Carriers](https://ieeexplore.ieee.org/document/8651985) - Sonia Lefnaoui, Samia Rebouh, Mounir Bouhedda, Madiha M. Yahoum, Salah Hanini (2018)

## Clinical trials

* [Development of a Novel Convolution Neural Network for Arrhythmia Classification (AI-ECG)](https://clinicaltrials.gov/ct2/show/NCT03662802) - Sanjeev Bhavnani MD, Scripps Clinic

* [Artificial Intelligence With Deep Learning and Genes on Cardiovascular Disease](https://clinicaltrials.gov/ct2/show/NCT03877614) - National Cheng-Kung University Hospital 

## Teams

* [Cardiac MRI analysis @ MIT](https://www.csail.mit.edu/research/cardiac-mri-analysis)

* [Arrhythmia detection @ Stanford University](https://stanfordmlgroup.github.io/projects/ecg2/)

## News, meta resources, and other further reading

* [Stanford computer scientists develop an algorithm that diagnoses heart arrhythmias with cardiologist-level accuracy](https://news.stanford.edu/2017/07/06/algorithm-diagnoses-heart-arrhythmias-cardiologist-level-accuracy/) - Taylor Kubota (2017)

* [How AI based arrhythmia detector can explain its decisions](https://medium.com/mawi-band/how-ai-based-arrhythmia-detector-can-explain-its-decisions-b4f433faa4a2) - Artem Bachynskyi (2018)

* [Machine learning overtakes humans in predicting death or heart attack](https://www.escardio.org/The-ESC/Press-Office/Press-releases/machine-learning-overtakes-humans-in-predicting-death-or-heart-attack) - European Society of Cardiology (2019)

* [Artificial Intelligence and Echocardiography](https://www.acc.org/latest-in-cardiology/articles/2019/06/18/07/43/artificial-intelligence-and-echocardiography) - Akhil Narang, Roberto M. Lang (2019)

* [Artificial Intelligence examining ECGs predicts irregular heartbeat, death risk](https://newsroom.heart.org/news/artificial-intelligence-examining-ecgs-predicts-irregular-heartbeat-death-risk) - American Heart Association (2019)

* [Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification!](https://towardsdatascience.com/applied-topological-data-analysis-to-deep-learning-hands-on-arrhythmia-classification-48993d78f9e6) - Dindin Meryll (2019)

* [Applications for Artificial Intelligence in Cardiovascular Imaging](https://www.dicardiology.com/article/applications-artificial-intelligence-cardiovascular-imaging) - Dave Fornell, Diagnostic and Interventional Cardiology (2019)