Multiclass arrhythmia classification using multimodal smartwatch photoplethysmography signals collected in real-life settings.

ABSTRACT: In the early stages of atrial fibrillation (AF), most cases are paroxysmal (pAF), making identification only possible with continuous and prolonged monitoring. With the advent of wearables, smartwatches equipped with photoplethysmographic (PPG) sensors are an ideal approach for continuous monitoring of pAF. There have been numerous studies demonstrating successful capture of pAF events, especially using deep learning. However, deep learning requires a large amount of data and independent testing on diverse datasets, to ensure the generalizability of the model, and most prior studies did not meet these requirements. Moreover, most prior studies using wearable-based PPG sensor data collection were limited either to controlled environments, to minimize motion artifacts, or to short duration d

SUBMITTER: Han D 

PROVIDER: S-EPMC11661413 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature