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Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysis

  • Introduction: Electrode misplacement and interchange errors are known problems when recording the 12‑lead electrocardiogram (ECG). Automatic detection of these errors could play an important role for improving clinical decision making and outcomes in cardiac care. The objectives of this systematic review and meta-analysis is to 1) study the impact of electrode misplacement on ECG signals and ECG interpretation, 2) to determine the most challenging electrode misplacements to detect using machine learning (ML), 3) to analyse the ML performance of algorithms that detect electrode misplacement or interchange according to sensitivity and specificity and 4) to identify the most commonly used ML technique for detecting electrode misplacement/interchange. This review analysed the current literature regarding electrode misplacement/interchange recognition accuracy using machine learning techniques. Method: A search of three online databases including IEEE, PubMed and ScienceDirect identified 228 articles, while 3 articles wereIntroduction: Electrode misplacement and interchange errors are known problems when recording the 12‑lead electrocardiogram (ECG). Automatic detection of these errors could play an important role for improving clinical decision making and outcomes in cardiac care. The objectives of this systematic review and meta-analysis is to 1) study the impact of electrode misplacement on ECG signals and ECG interpretation, 2) to determine the most challenging electrode misplacements to detect using machine learning (ML), 3) to analyse the ML performance of algorithms that detect electrode misplacement or interchange according to sensitivity and specificity and 4) to identify the most commonly used ML technique for detecting electrode misplacement/interchange. This review analysed the current literature regarding electrode misplacement/interchange recognition accuracy using machine learning techniques. Method: A search of three online databases including IEEE, PubMed and ScienceDirect identified 228 articles, while 3 articles were included from additional sources from co-authors. According to the eligibility criteria, 14 articles were selected. The selected articles were considered for qualitative analysis and meta-analysis. Results: The articles showed the effect of lead interchange on ECG morphology and as a consequence on patient diagnoses. Statistical analysis of the included articles found that machine learning performance is high in detecting electrode misplacement/interchange except left arm/left leg interchange. Conclusion: This review emphasises the importance of detecting electrode misplacement detection in ECG diagnosis and the effects on decision making. Machine learning shows promise in detecting lead misplacement/interchange and highlights an opportunity for developing and operationalising deep learning algorithms such as convolutional neural network (CNN) to detect electrode misplacement/interchange.show moreshow less

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Metadaten
Author:Daniel GüldenringORCiD, Khaled Rjoob, Raymond R. BondORCiD, Dewar D. FinlayORCiD, Victoria McGilligan, Stephen J. Leslie, Ali S. Rababah, Aleeha Iftikhar, Charles Knoery, Anne McShane, Aaron Peace
DOI:https://doi.org/10.1016/j.jelectrocard.2020.08.013
Identifier:0022-0736 OPAC HS OPAC extern
Parent Title (English):Journal of Electrocardiology
Publisher:Elsevier
Place of publication:New York
Document Type:Article
Language:English
Date of Publication (online):2020/09/01
Year of first Publication:2020
Tag:Biomedizinische Signalverarbeitung; EKG
ECG
Volume:62.
Number of pages:8 Seiten
First Page:116
Last Page:123
Institutes:Fakultät Elektrotechnik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Publication Lists:Güldenring, Daniel
Publication reviewed:begutachtet
Release Date:2021/02/16
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