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

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.

Deep learning for COVID‐19 contamination analysis and prediction using ECG images on Raspberry Pi 4

“…In various papers, the state‐of‐the‐art study on deep learning has shown satisfactory results in detection, classification, and prediction tasks on medical images in general and ECG in particular. Several types of research have been done using different techniques to detect and automatically predict coronavirus contamination and other pathologies 44–47 …”

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A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

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Completing the Cabrera Circle: deriving adaptable leads from ECG limb leads by combining constraints with a correction factor

“…Recently, methods from the field of deep learning (DL) became popular for ECG-related tasks (Somani et al 2021, Petmezas et al 2022. For example, DL-based ECG classifications show promising results reaching accuracy similar to human experts (Hannun et al 2019, Ribeiro et al 2020, Reyna et al 2021.…”