https://github.com/yassin522/heartbeat-categorization
This project is aimed at developing a machine learning model that can accurately classify heartbeats as either normal or abnormal. The model is trained on a dataset of ECG (electrocardiogram) signals, which were collected from patients and labeled by medical professionals.
https://github.com/yassin522/heartbeat-categorization
cnn deep-learning keras machine-learning scikit-learn tensorflow
Last synced: 2 months ago
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This project is aimed at developing a machine learning model that can accurately classify heartbeats as either normal or abnormal. The model is trained on a dataset of ECG (electrocardiogram) signals, which were collected from patients and labeled by medical professionals.
- Host: GitHub
- URL: https://github.com/yassin522/heartbeat-categorization
- Owner: Yassin522
- Created: 2023-01-11T11:26:53.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-01T17:22:54.000Z (over 2 years ago)
- Last Synced: 2024-04-20T13:44:17.912Z (about 2 years ago)
- Topics: cnn, deep-learning, keras, machine-learning, scikit-learn, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 1.59 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Heartbeat Categorization
## About The Project
Heartbeat Categorization is a machine learning project focused on classifying heartbeats as normal or abnormal. Utilizing a dataset of ECG (electrocardiogram) signals, this model aims to assist in the early detection and diagnosis of cardiac abnormalities. The dataset comprises ECG recordings collected from patients and annotated by medical professionals, ensuring the accuracy and reliability of the training data.
## Key Features
- **ECG Signal Analysis:** Processes ECG data to extract meaningful features for classification.
- **Machine Learning Model:** Employs advanced algorithms to distinguish between normal and abnormal heartbeats.
- **Medical Data Handling:** Carefully handles sensitive medical data, maintaining patient confidentiality and data integrity.
## Technologies Used
- Python 3.6 or higher
- NumPy
- Pandas
- Scikit-learn
- Keras (with TensorFlow backend)
## Getting Started
Follow these instructions to set up the project on your local machine for development and testing purposes.
### Prerequisites
Ensure you have the following tools and libraries installed:
- Python 3.6 or higher
- NumPy
- Pandas
- Scikit-learn
- Keras with TensorFlow backend
You can install them using pip:
```bash
pip install numpy pandas scikit-learn keras tensorflow
```