Introduction Electrode misplacement can adversely affect Electrocardiogram (ECG) morphology and as a consequence, it can affect interpretation and patient diagnosis. Lead misplacement in ECG acquisition can occur in 40% to 60% of the time; which can result. in false positives (Type-I errors) and false negatives (Type-II errors). V1 and V2 electrodes are commonly misplaced high and wide of their correct positions. Vertically misplaced leads V1 and V2 can conceal real STEMI (ST Elevation Myocardial Infarction) events or emulate a false positive. Lead misplacement detection plays an important role to improve ECG data quality and clinical decision making in cardiac care. The aim of the current work was to develop a computer model using deep learning to detect misplacement of leads V1 and V2 for three different types of patients: 1) old Myocardial Infarction (MI) patients, 2) Left Ventricular Hypertrophy (LVH) Patients and 3) normal sinus rhythm patients, and to find in which patient type the ‘general’ patient-type-agnostic computer model achieves the highest performance.
Methods 12-lead ECGs for 453 subjects, (normal n=151, LVH n=151, MI n=151) were extracted from body surface potential maps. V1 and V2 leads acquired from the second intercostal space (ICS) are considered as incorrect placement and leads from the fourth (IC) are considered as correct. The computer model was developed using deep learning algorithm (bidirectional long short-term memory network (BLSTM)) to detect V1 and V2 misplacement. The computer model was trained on three datasets together and tested on each dataset separately. In each dataset, data were divided into 67% for training and 33% for testing. The prevalence was 50% for correct and incorrect leads.
Results Computer model accuracy using BLSTM of V1 and V2 misplacement detection was 93.0% in normal dataset, 93.0% in MI dataset while the accuracy was 91.0% in LVH dataset.
Conclusion Computer model got a high performance to detect V1 and V2 misplacement in normal and MI ECGs datasets, while the performance decreased in LVH dataset to 91.0%. In future we need to consider MI infarct sites such as anterior, inferior etc to increase accuracy.
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