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Review
. 2021 Aug 7;2(3):416-423.
doi: 10.1093/ehjdh/ztab048. eCollection 2021 Sep.

Deep learning analysis of resting electrocardiograms for the detection of myocardial dysfunction, hypertrophy, and ischaemia: a systematic review

Ghalib Al Hinai  1 , Samer Jammoul  1 , Zara Vajihi  2 , Jonathan Afilalo  1   3
Affiliations
Review

Deep learning analysis of resting electrocardiograms for the detection of myocardial dysfunction, hypertrophy, and ischaemia: a systematic review

Ghalib Al Hinai et al. Eur Heart J Digit Health. .

Erratum in

  • Erratum.
    [No authors listed] [No authors listed] Eur Heart J Digit Health. 2021 Nov 21;3(1):115-116. doi: 10.1093/ehjdh/ztab098. eCollection 2022 Mar. Eur Heart J Digit Health. 2021. PMID: 36713986 Free PMC article.

Abstract

The aim of this review was to assess the evidence for deep learning (DL) analysis of resting electrocardiograms (ECGs) to predict structural cardiac pathologies such as left ventricular (LV) systolic dysfunction, myocardial hypertrophy, and ischaemic heart disease. A systematic literature search was conducted to identify published original articles on end-to-end DL analysis of resting ECG signals for the detection of structural cardiac pathologies. Studies were excluded if the ECG was acquired by ambulatory, stress, intracardiac, or implantable devices, and if the pathology of interest was arrhythmic in nature. After duplicate reviewers screened search results, 12 articles met the inclusion criteria and were included. Three articles used DL to detect LV systolic dysfunction, achieving an area under the curve (AUC) of 0.89-0.93 and an accuracy of 98%. One study used DL to detect LV hypertrophy, achieving an AUC of 0.87 and an accuracy of 87%. Six articles used DL to detect acute myocardial infarction, achieving an AUC of 0.88-1.00 and an accuracy of 83-99.9%. Two articles used DL to detect stable ischaemic heart disease, achieving an accuracy of 95-99.9%. Deep learning models, particularly those that used convolutional neural networks, outperformed rules-based models and other machine learning models. Deep learning is a promising technique to analyse resting ECG signals for the detection of structural cardiac pathologies, which has clinical applicability for more effective screening of asymptomatic populations and expedited diagnostic work-up of symptomatic patients at risk for cardiovascular disease.

Keywords: Artificial intelligence; Coronary artery disease; Deep learning; Electrocardiogram; Heart failure; Left ventricular hypertrophy; Myocardial infarction.

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Figures

None
Graphical abstract
Figure 1
Figure 1
Deep neural network. Sample architecture of a deep convolutional neural network composed of a first input layer for receiving the electrocardiogram signals, four hidden convolutional layers with multiple kernels for analysing the electrocardiogram signal features, and a dense output layer for generating the predicted left ventricular function and mass.
Figure 2
Figure 2
Flow diagram.
See this image and copyright information in PMC

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