Abstract 4130756: Two Leads are All We Need: Optimizing Choice of Electrocardiogram Leads for Artificial Intelligence-based Based Detection of Left Ventricular Systolic Dysfunction

Abstract

Introduction: Artificial intelligence (AI) models built with convolutional neural networks (CNN) accurately detect left ventricular systolic dysfunction (LVSD) from 12-lead electrocardiogram (ECG). While AI models can also detect LVSD from 1-lead (but with lower accuracy), the optimal numbers and combinations of the leads remain unclear. Aims: To identify the optimal numbers and combinations of ECG leads for detecting LVSD. Methods: A total of 75,033 ECGs recorded within 14 days of an echocardiogram were collected. The data was randomly divided into derivation, validation, and test sets in a 5:2:3 ratio without patient overlap. The same split was used throughout the study. While all available ECGs were included in derivation and validation sets, the test set included 1 ECG per patient closest to the echocardiogram. CNN models were trained to detect LVSD, defined as left ventricular ejection fraction <40%, using all possible combinations of 1-, 2-, and 3-lead extracted from 12-lead ECG. The final model for each lead combination was chosen according to the area under the receiver operating curve (AUROC) on the validation set across the 20 epochs. The models were trained 4 times to evaluate the variance caused by initialization vectors. Results: The 12-lead model showed AUROC of 0.893 (95%CI, 0.887-0.899). Multiple models trained with 2-lead achieved similar performance, with aVR-V4 displaying the highest AUROC (0.892; 95%CI, 0.889-0.895), followed by I-V4 (0.889; 95%CI, 0.883-0.895) and I-II (0.888; 95%CI, 0.884-0.893). The results were similar for 3-lead (aVR-aVL-V4: AUROC 0.895; 95%CI, 0.893-0.897, I-aVR-V4: 0.892; 95%CI, 0.889-0.896, and I-II-V4: 0.891; 95%CI, 0.886-0.895). The choice of leads greatly affected the performance. For the 1-lead models, aVR showed the highest AUROC (0.876; 95%CI, 0.873-0.879), but III only achieved an AUROC of (0.766; 95%CI, 0.761-0.772). The combinations, including a chest lead and a limb lead (especially aVR), tend to achieve higher performance (Fig 1). Conclusion: A combination of 2-lead was sufficient to achieve comparable accuracy as the 12-lead for the AI model to detect LVSD. Wearable devices collecting a combination of a limb and a chest lead may enhance the detection of LVSD.