Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices.

ABSTRACT: Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but frequently obtain noisy ECGs. We report a novel strategy that automates the detection of hidden cardiovascular diseases, such as LVSD, adapted for noisy single-lead ECGs obtained on wearable and portable devices. We use 385,601 ECGs for development of a standard and noise-adapted model. For the noise-adapted model, ECGs are augmented during training with random gaussian noise within four distinct frequency ranges, each emulating real-world noise sources. Both models perform comparably on standard ECGs with an AUROC of 0.90. The noise-adapted model performs significantly better on the same test set augmented with f

SUBMITTER: Khunte A 

PROVIDER: S-EPMC10336107 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature