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

Khaled Rjoob, RR Bond, D Finlay, V. E. McGilligan, Stephen James Leslie, Ali Rababah, Aleeha Iftikhar, D Guldenring, Charles Knoery, Anne McShane, Aaron Peace

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6 Citations (Scopus)
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Abstract

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 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.

Original languageEnglish
Article numbere25347
Pages (from-to)1-10
Number of pages10
JournalJMIR Medical Informatics
Volume9
Issue number4
DOIs
Publication statusPublished (in print/issue) - 16 Apr 2021

Bibliographical note

Funding Information:
This work was supported by the European Union’s INTERREG VA program, managed by the Special EU Programmes Body (SEUPB). The views and opinions expressed in this study do not necessarily reflect those of the European Commission or the SEUPB.

Funding Information:
This work was supported by the European Union?s INTERREG VA program, managed by the Special EU Programmes Body (SEUPB). The views and opinions expressed in this study do not necessarily reflect those of the European Commission or the SEUPB.

Publisher Copyright:
©Khaled Rjoob, Raymond Bond, Dewar Finlay, Victoria McGilligan, Stephen J Leslie, Ali Rababah, Aleeha Iftikhar, Daniel Guldenring, Charles Knoery, Anne McShane, Aaron Peace.

Keywords

  • deep learning
  • ECG interpretation
  • electrode misplacement
  • feature engineering
  • machine learning
  • medical error
  • Myocardial infarction
  • Physicians
  • ECG
  • Cardiovascular disease
  • Feature engineering
  • Deep learning
  • Engineering
  • Electrode misplacement
  • Machine learning
  • Myocardial

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