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

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

Anup Das, Francky Catthoor, Siebren Schaafsma and IEEE
2018 IEEE/ACM INTERNATIONAL CONFERECE ON CONNECTED HEALTH: APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES (CHASE), pp 69-74
01 Jan 2018
DOI: https://doi.org/10.1145/3278576.3278598
url
http://arxiv.org/abs/1908.06865View
SubmittedOpen Access (License Unspecified),  Open
url
https://doi.org/10.1145/3278576.3278598View
Published, Version of Record (VoR) Open

Abstract

Engineering Engineering, Biomedical Life Sciences & Biomedicine Medical Informatics Science & Technology Technology
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.

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24 citations in Web of Science
34 citations in Scopus

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Engineering, Biomedical
Medical Informatics
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