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Review
. 2022 Aug 15;10(8):e38454.
doi: 10.2196/38454.

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Georgios Petmezas  1 , Leandros Stefanopoulos  1 , Vassilis Kilintzis  1 , Andreas Tzavelis  2 , John A Rogers  3 , Aggelos K Katsaggelos  4 , Nicos Maglaveras  1
Affiliations
Review

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Georgios Petmezas et al. JMIR Med Inform. .

Abstract

Background: Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient's health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals.

Objective: This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications.

Methods: The PubMed search engine was systematically searched by combining "deep learning" and keywords such as "ecg," "ekg," "electrocardiogram," "electrocardiography," and "electrocardiology." Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches.

Results: We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models.

Conclusions: We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.

Keywords: CNN; ECG; ECG databases; LSTM; ResNet; clinical decision; convolutional neural networks; decision support; deep learning; diagnostic tools; electrocardiogram; long short-term memory; residual neural network.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Flow diagram of the literature search. DL: deep learning; ECG: electrocardiogram.
Figure 2
Figure 2
The co-occurrence network for the “clinical issues” cluster.
Figure 3
Figure 3
The co-occurrence network for the “methods and tools” cluster.
Figure 4
Figure 4
The co-occurrence network for the “study characteristics” cluster.
See this image and copyright information in PMC

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