TomMakesThings/Semi-Supervised-ECG-Classifier
Undergraduate group project in which we built an ECG classifier using a TCN-CNN with 97% accuracy
This project helps medical practitioners or researchers quickly identify specific heart conditions from electrocardiogram (ECG) heartbeat data. You input raw ECG time series data, and it outputs a classification indicating different types of arrhythmia. This tool is designed for cardiologists, medical technicians, or researchers analyzing heart rhythm patterns.
No commits in the last 6 months.
Use this if you need an accurate, automated system to classify various heart arrhythmias from ECG readings to assist in diagnosis or research.
Not ideal if you require a system that explicitly uses self-supervised clustering for improved accuracy, as this particular approach did not yield significant gains here.
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Oct 02, 2021
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