jorgesandoval/heartbeat-classification-cnn
An advanced ECG anomaly detection system using deep learning. This repository contains a CNN autoencoder trained on the PTBDB dataset to identify abnormal heart rhythms. It employs various loss functions for model optimization and provides comprehensive visualizations of the results.
This project helps cardiologists or medical researchers automatically identify abnormal heart rhythms from electrocardiogram (ECG) data. It takes raw ECG signals as input, processes them, and then outputs classifications of normal or anomalous heartbeats with detailed performance metrics. This tool is designed for medical practitioners and researchers who analyze large volumes of ECG data.
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Use this if you need an automated, highly accurate system to detect anomalies in ECG data for research or diagnostic support.
Not ideal if you need a real-time, production-ready diagnostic tool for clinical use without further integration and validation.
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Jupyter Notebook
License
MIT
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Last pushed
Oct 23, 2023
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