A multi-layer perceptron trained on short-time Fourier transform sparse signatures of ECG heartbeats is reported to reach 95.7% average classification accuracy on MIT-BIH, but the evaluation uses a beat-level split with patient leakage.
Heartbeat classification using disease-specific feature selection,
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Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG
A multi-layer perceptron trained on short-time Fourier transform sparse signatures of ECG heartbeats is reported to reach 95.7% average classification accuracy on MIT-BIH, but the evaluation uses a beat-level split with patient leakage.