Forecasting imminent atrial fibrillation in long-term electrocardiogram recordings.

ABSTRACT:

Background

Despite the morbidity associated with acute atrial fibrillation (AF), no models currently exist to forecast its imminent onset. We sought to evaluate the ability of deep learning to forecast the imminent onset of AF with sufficient lead time, which has important implications for inpatient care.

Methods

We utilized the Physiobank Long-Term AF Database, which contains 24-h, labeled ECG recordings from patients with a history of AF. AF episodes were defined as ≥5 min of sustained AF. Three deep learning models incorporating convolutional and transformer layers were created for forecasting, with two models focusing on the predictive nature of sinus rhythm segments and AF epochs separately preceding an AF episode, and one model utilizing all preceding waveform as input

SUBMITTER: Rooney SR 

PROVIDER: S-EPMC10841237 | biostudies-literature | 2023 Nov-Dec

REPOSITORIES: biostudies-literature