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Reliable Deep Learning–Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and Validation

  • Background: A 12-lead electrocardiogram (ECG) is the most commonly used method to diagnose patients with cardiovascular diseases. However, there are a number of possible misinterpretations of the ECG that can be caused by several different factors, such as the misplacement of chest electrodes. Objective: The aim of this study is to build advanced algorithms to detect precordial (chest) electrode misplacement. Methods: In this study, we used traditional machine learning (ML) and deep learning (DL) to autodetect the misplacement of electrodes V1 and V2 using features from the resultant ECG. The algorithms were trained using data extracted from high-resolution body surface potential maps of patients who were diagnosed with myocardial infarction, diagnosed with left ventricular hypertrophy, or a normal ECG. Results: DL achieved the highest accuracy in this study for detecting V1 and V2 electrode misplacement, with an accuracy of 93.0% (95% CI 91.46-94.53) for misplacement in the second intercostal space. The performance of DLBackground: A 12-lead electrocardiogram (ECG) is the most commonly used method to diagnose patients with cardiovascular diseases. However, there are a number of possible misinterpretations of the ECG that can be caused by several different factors, such as the misplacement of chest electrodes. Objective: The aim of this study is to build advanced algorithms to detect precordial (chest) electrode misplacement. Methods: In this study, we used traditional machine learning (ML) and deep learning (DL) to autodetect the misplacement of electrodes V1 and V2 using features from the resultant ECG. The algorithms were trained using data extracted from high-resolution body surface potential maps of patients who were diagnosed with myocardial infarction, diagnosed with left ventricular hypertrophy, or a normal ECG. Results: DL achieved the highest accuracy in this study for detecting V1 and V2 electrode misplacement, with an accuracy of 93.0% (95% CI 91.46-94.53) for misplacement in the second intercostal space. The performance of DL in the second intercostal space was benchmarked with physicians (n=11 and age 47.3 years, SD 15.5) who were experienced in reading ECGs (mean number of ECGs read in the past year 436.54, SD 397.9). Physicians were poor at recognizing chest electrode misplacement on the ECG and achieved a mean accuracy of 60% (95% CI 56.09-63.90), which was significantly poorer than that of DL (P<.001). Conclusions: DL provides the best performance for detecting chest electrode misplacement when compared with the ability of experienced physicians. DL and ML could be used to help flag ECGs that have been incorrectly recorded and flag that the data may be flawed, which could reduce the number of erroneous diagnoses.show moreshow less

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Metadaten
Author:Khaled Rjoob, Raymond R. BondORCiD, Dewar D. FinlayORCiD, Victoria McGilligan, Stephen J. Leslie, Ali S. Rababah, Aleeha Iftikhar, Daniel GüldenringORCiD, Charles Knoery, Anne McShane, Aaron Peace
DOI:https://doi.org/10.2196/25347
Identifier:2291-9694 OPAC HS OPAC extern
Parent Title (English):JMIR Medical Informatics
Publisher:JMIR Publications
Place of publication:Toronto
Document Type:Article
Language:English
Date of Publication (online):2021/04/16
Year of first Publication:2021
Tag:Biomedizinische Signalverarbeitung; EKG; ECG
Volume:9
Issue:4
Article Number:e25347
Number of pages:10 Seiten
First Page:1
Last Page:10
Institutes:Fakultät Elektrotechnik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Research focus:Eigenständige Forschungsfelder
Publication Lists:Güldenring, Daniel
Publication reviewed:begutachtet
Release Date:2021/06/15
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