Comprehensive electrocardiographic diagnosis based on deep learning

LIH, Oh Shu, JAHMUNAH, V., SAN, Tan Ru, CIACCIO, Edward J., YAMAKAWA, Toshitaka, TANABE, Masayuki, KOBAYASHI, Makiko, FAUST, Oliver and ACHARYA, U Rajendra (2020). Comprehensive electrocardiographic diagnosis based on deep learning. Artificial Intelligence in Medicine, p. 101789.

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Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified on manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals.

Item Type: Article
Additional Information: ** Article version: AM ** Embargo end date: 31-12-9999 ** From Elsevier via Jisc Publications Router ** Licence for AM version of this article: This article is under embargo with an end date yet to be finalised. **Journal IDs: issn 09333657 **History: issue date 20-01-2020; accepted 31-12-2019
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Page Range: p. 101789
SWORD Depositor: Colin Knott
Depositing User: Colin Knott
Date Deposited: 27 Jan 2020 11:27
Last Modified: 17 Mar 2021 16:16

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