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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Background: A 12-lead electrocardiogram (ECG) is the most common method that is used to help diagnose patients with cardiovascular diseases. However, there are a number of misinterpretations of the ECG caused by several different factors such as the misplacement of chest electrodes. Objective: This research is the first experiment to build advanced algorithms to detect precordial (chest) electrode misplacement. Method: In this article we used traditional machine learning (ML) and deep learning (DL) to auto-detect 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 consisting of patients who were diagnosed with myocardial infarction, left ventricular hypertrophy or normal. 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. DL performance in the second intercostal space was benchmarked with physicians (n=11 and age=47.3±15.5) who are experienced in reading ECGs (mean number of ECGs read in the past year = 436.54±397.9). Physicians were poor at recognising chest electrode misplacement on the ECG and achieved a mean accuracy of 60% [95%CI=56.09,63.90] which was significantly poorer when compared to DL (P<.001). Conclusion: DL provides the best performance for detecting chest electrode misplacement when compared to the ability of experienced physicians. Clinical Impact: DL and ML could be used to help flag ECGs that have been incorrectly recorded and that the data maybe be flawed, which could reduce an erroneous diagnosis.
Original languageEnglish
Article numbere25347
Pages (from-to)1-10
Number of pages10
JournalJMIR Medical Informatics
Issue number4
Publication statusPublished - 16 Apr 2021


  • deep learning
  • ECG interpretation
  • electrode misplacement
  • feature engineering
  • machine learning
  • medical error


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