AlirezaKhodabakhsh/ECGNet
ECGNet, leveraging PyTorch, classifies ECG signals with 96% accuracy, using a streamlined model of around 1300 parameters, trained on Kaggle's PTB Diagnostic ECG Database
This project helps medical professionals quickly identify normal versus abnormal heart rhythms from electrocardiogram (ECG) signals. You input ECG recordings, and it provides a classification indicating whether the heart rhythm is normal or if there's a potential abnormality. Cardiac technicians, cardiologists, and emergency room physicians could use this tool to assist with initial diagnostic screenings.
No commits in the last 6 months.
Use this if you need an efficient and accurate automated system to classify ECG signals for preliminary cardiac diagnostics.
Not ideal if you require a comprehensive diagnostic report beyond basic normal/abnormal classification or if you need to identify specific cardiac conditions rather than just general abnormalities.
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Last pushed
Feb 22, 2024
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