Adapting AI for 24/7 ECG monitoring: Holter-based detection of LV dysfunction

作者：D Hu, K Liu, K E Mangold, T Wagner, Samir Awasthi, J C Cruz, M K Ranganathan, A J Deshmukh, F Lopez-Jimenez, P A Friedman, P A Noseworthy, Z I Attia · 发表于：European Heart Journal · 年份：2025 · DOI：10.1093/eurheartj/ehaf784.4452 · 研究领域：ECG Monitoring and Analysis、Non-Invasive Vital Sign Monitoring、Phonocardiography and Auscultation Techniques

Abstract Background Artificial intelligence (AI) models trained on 12-lead ECGs effectively detect left ventricular systolic dysfunction (LVSD; left ventricular ejection fraction [LVEF] <=40%). Continuous ECG monitoring via Holter recordings provides an opportunity for opportunistic screening for structural heart disease beyond rhythm disorders. We hypothesized that a lead-invariant version of the 12-lead AI model would enable a Holter monitor to screen for both arrhythmias and ventricular dysfunction. Methods We retrospectively analyzed continuous Holter ECGs from 17,665 patients who underwent a Holter and transthoracic echocardiogram (TTE) within 30 days of each other at Mayo Clinic. From each Holter, a random 20-minute of valid (non-flatline/lead disconnect) ECG segment was extracted and analyzed for LVSD detection using the adapted lead-invariant AI model. To evaluate stability, we examined model performance across different time points of the day, presenting results as area under the receiver operating characteristic curve (AUC) over time. Moreover, we illustrated the model’s robustness to noisy data by comparing its performance on raw ECG signals with that on bandpass-filtered inputs. Results Among 17,665 patients (mean age 59 years, 48.57% female), 4.96% had an LVEF <=40%. The AI model demonstrated strong predictive performance (20-minute segment AUC 0.90, mean prediction of 24-hour AUC 0.92). Analysis of results over time (Figure) revealed temporal patte...