Using a machine learning algorithm to detect depressed ejection fraction from a single-lead ECG. (14th October 2021)

• Record Type: Journal Article
• Authors: Guo, L; Le, L; Kieu, S; Tiwari, U; Currie, C; Shenthar, J; Padmanabhan, D; Pressman, G; Maidens, J; Saltman, A
• Is Part Of: European heart journal. Volume 42(2021)Supplement 1
• Publication Date: 2021-10-14
• Subjects: Artificial Intelligence (Machine Learning, Deep Learning); Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehab724.3065
• Languages: English
• ISSNs: 0195-668X

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. FUNDunding Acknowledgement: Type of funding sources: Private company. Main funding source(s): Eko Health