dave-fernandes/ECGClassifier
CNN, RNN, and Bayesian NN classification for ECG time-series (using TensorFlow in Swift and Python)
This project helps classify individual heartbeats from ECG recordings to identify normal patterns or different types of arrhythmias. You input segmented ECG time-series data for a single heartbeat, and it outputs a classification label indicating the heart rhythm type. It is designed for medical researchers or practitioners who need to analyze ECG data for cardiac anomaly detection.
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Use this if you are analyzing individual heartbeat segments from ECGs and need to automatically categorize them as normal or one of four specific arrhythmia types.
Not ideal if you need to analyze continuous, unsegmented ECG recordings or require classification of different cardiac conditions beyond the specified arrhythmia types.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Jul 28, 2019
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