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 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.

Deep learning consistently detects misplaced chest electrodes when recording the electrocardiogram: An error that is commonly undetected by physicians (Preprint)

BACKGROUND A 12-lead electrocardiogram (ECG) is the most common method to diagnose cardiovascular diseases such as acute myocardial infarction. However, there are a number of misinterpretations of the ECG caused by several different factors. One influential factor can take place during ECG acquisition where chest electrodes are misplaced. OBJECTIVE This research is the first experiment to build advanced algorithms to detect precordial (chest) electrode misplacement. METHODS 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). CONCLUSIONS 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.