Novel application of convolutional neural networks for artificial intelligence-enabled modified moving average analysis of P-, R-, and T-wave alternans for detection of risk for atrial and ventricular arrhythmias

作者：Bruce D. Nearing, Richard L. Verrier · 发表于：Journal of Electrocardiology · 年份：2023 · DOI：10.1016/j.jelectrocard.2023.12.012 · 被引用次数：6 · 研究领域：Cardiac electrophysiology and arrhythmias、ECG Monitoring and Analysis、Atrial Fibrillation Management and Outcomes

Background T-wave alternans (TWA) analysis was shown in >14,000 individuals studied worldwide over the past two decades to be a useful tool to assess risk for cardiovascular mortality and sudden arrhythmic death. TWA analysis by the modified moving average (MMA) method is FDA-cleared and CMS-reimbursed (CAG-00293R2). Objective Because the MMA technique is inherently suitable for dynamic tracking of alternans levels, it was selected for development of artificial intelligence (AI)-enabled algorithms using convolutional neural networks (CNN) to achieve rapid, efficient, and accurate assessment of P-wave alternans (PWA), R-wave alternans (RWA), and TWA. Methods The novel application of CNN algorithms to enhance MMA analysis generated efficient and powerful pattern-recognition algorithms for highly accurate alternans quantification. Algorithm reliability and accuracy were verified using simulated ECGs achieving R 2 ≥ 0.99 ( p < 0.01) in response to noise inputs and artifacts that emulate real-life conditions. Results Accuracy of the new AI-MMA algorithms in TWA analysis ( n = 5) was significantly improved over unsupervised, automated MMA output ( p = 0.036) and did not differ from conventional MMA analysis with expert overreading ( p = 0.21). Accuracy of AI-MMA in PWA analysis ( n = 45) was significantly improved over unsupervised, automated MMA output ( p < 0.005) and did not differ from conventional MMA analysis with expert overreading ( p = 0.89). TWA and PWA by AI-MMA were cor...