Detection of Abnormalities in Electrocardiogram (ECG) using Deep Learning

João Pestana, David Belo, Hugo Gamboa

2020

Abstract

The Electrocardiogram (ECG) cyclic behaviour gives insights on a subject’s emotional, behavioral and cardiovascular state, but often presents abnormal events. The noise made during the acquisition, and presence of symptomatic patterns are examples of anomalies. The proposed Deep Learning framework learns the normal ECG cycles and detects its deviation when the morphology changes. This technology is tested in two different settings having an autoencoder as base for learning features: detection of three different types of noise, and detection of six arrhythmia events. Two Convolutional Neural Network (CNN) algorithms were developed for noise detection achieving accuracies of 98.18% for a binary-class model and 70.74% for a multi-class model. The development of the arrhythmia detection algorithm also included a Gated Recurrent Unit (GRU) for grasping time-dependencies reaching an accuracy of 56.85% and an average sensitivity of 61.13%. The process of learning the abstraction of a ECG signal, currently sacrifices the accuracy for higher generalization, better discriminating the presence of abnormal events in ECG than detecting different types of events. Further improvement could represent a major contribution in symptomatic screening, active learning of unseen events and the study of pathologies to support physicians in the future.

Paper Citation

in Harvard Style

Pestana J., Belo D. and Gamboa H. (2020). Detection of Abnormalities in Electrocardiogram (ECG) using Deep Learning. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS; ISBN 978-989-758-398-8, SciTePress, pages 236-243. DOI: 10.5220/0008967302360243

in Bibtex Style

@conference{biosignals20,
author={João Pestana and David Belo and Hugo Gamboa},
title={Detection of Abnormalities in Electrocardiogram (ECG) using Deep Learning},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS},
year={2020},
pages={236-243},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008967302360243},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS
TI - Detection of Abnormalities in Electrocardiogram (ECG) using Deep Learning
SN - 978-989-758-398-8
AU - Pestana J.
AU - Belo D.
AU - Gamboa H.
PY - 2020
SP - 236
EP - 243
DO - 10.5220/0008967302360243
PB - SciTePress