Atrial fibrillation detection from raw photoplethysmography waveforms: A deep learning application.

ABSTRACT:

Background

Atrial fibrillation (AF), a common cause of stroke, often is asymptomatic. Smartphones and smartwatches can detect AF using heart rate patterns inferred using photoplethysmography (PPG); however, enhanced accuracy is required to reduce false positives in screening populations.

Objective

The purpose of this study was to test the hypothesis that a deep learning algorithm given raw, smartwatch-derived PPG waveforms would discriminate AF from normal sinus rhythm better than algorithms using heart rate alone.

Methods

Patients presenting for cardioversion of AF (n = 51) were given wrist-worn fitness trackers containing PPG sensors (Jawbone Health). Standard 12-lead electrocardiograms over-read by board-certified cardiac electrophysiologists were used as the refer

SUBMITTER: Aschbacher K 

PROVIDER: S-EPMC8183963 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature