Using a machine learning algorithm to detect depressed ejection fraction from a single-lead ECG

Abstract

Abstract Background Multiple studies demonstrate the benefit of intervention for left ventricular ejection fraction (LVEF) below 40%, so the development of a low ejection fraction algorithm to detect LVEF below 40% can aid in early screening of initial asymptomatic Heart Failure with reduced Ejection Fraction (HFrEF). Objective To demonstrate the performance of a low ejection fraction algorithm using single-lead ECG data to detect LVEF below 40%. Methods We collected 1325 single-lead ECG recordings (15s duration) at various chest positions using an electronic stethoscope from 197 patients. We analyzed these ECG recordings using a deep neural network model trained on individual leads extracted from a 12-lead ECG to discriminate left ventricular ejection fractions (EFs) above or below different thresholds. We compared the model output to ejection fraction measured using echocardiograms. Results Across all recordings from all patients, we obtained an AUROC of 0.89, with a sensitivity of 88% and specificity of 74% using a model output threshold of 0.35 (Figure 1). The AUROC of recordings taken at different orientations and stances ranged from 0.85 to 0.92 (Table 1), with a sensitivity of at least 78% and specificity of at least 66% at any orientation. Conclusion Using a single lead ECG measured by an electronic stethoscope and a deep neural network model, we were able to detect depressed ejection fraction (≤40%) with a sensitivity of 88% and specificity of 74%. This work demonstrates the utility of a low-cost electronic stethoscope and machine learning for early screening and detection of depressed left ventricular ejection fraction. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Eko Health