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

arXiv: https://arxiv.org/abs/1908.06865 · HTML: https://mlsanity.com/paper/1908.06865/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants](https://mlsanity.com/paper/2202.10570/) (2022)
- [Evaluating the Impact of Loss Function Variation in Deep Learning for Classification](https://mlsanity.com/paper/2210.16003/) (2022)

## Abstract (arXiv)

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.