  Heartbeat classification using electrocardiogram (ECG) data is a vital
assistive technology for wearable health solutions. We propose heartbeat
feature classification based on a novel sparse representation using
time-frequency joint distribution of ECG. Fundamental to this is a multi-layer
perceptron, 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 of
22% over state-of-the-art approaches. Additionally, ECG sparse distributed
representations generates only 3.7% false negatives, reduction of 89% with
respect to existing ECG signal classification techniques.
