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

## TL;DR

This paper introduces a novel ECG heartbeat classification method using time-frequency joint distribution features and a multi-layer perceptron, achieving high accuracy and significantly reducing false negatives in wearable health applications.

## Contribution

The study presents a new sparse representation of ECG signals combined with a multi-layer perceptron for improved heartbeat classification accuracy.

## Key findings

- Achieved 95.7% classification accuracy.
- Reduced false negatives to 3.7%.
- Outperformed existing methods by 22% in accuracy.

## Abstract

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.