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Heartbeat classification using electrocardiogram (ECG) data is a vitalassistive technology for wearable health solutions. We propose heartbeatfeature classification based on a novel sparse representation usingtime-frequency joint distribution of ECG. Fundamental to this is a multi-layerperceptron, which incorporates these signatures to detect cardiac arrhythmia.This approach is validated with ECG data from MIT-BIH arrhythmia database.Results show that our approach has an average 95.7% accuracy, an improvement of22% over state-of-the-art approaches. Additionally, ECG sparse distributedrepresentations generates only 3.7% false negatives, reduction of 89% withrespect to existing ECG signal classification techniques.

Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG