SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals

arXiv:2404.15353v1 Announce Type: cross
Abstract: Atrial fibrillation (AF), a common cardiac arrhythmia, significantly increases the risk of stroke, heart disease, and mortality. Photoplethysmography (PPG) offers a promising solution for continuous AF monitoring, due to its cost efficiency and integration into wearable devices. Nonetheless, PPG signals are susceptible to corruption from motion artifacts and other factors often encountered in ambulatory settings. Conventional approaches typically discard corrupted segments or attempt to reconstruct original signals, allowing for the use of standard machine learning …

abstract accuracy architecture arxiv continuous cost cs.ai cs.lg detection devices disease dnn eess.sp efficiency heart disease integration monitoring mortality quality risk signal solution stroke type wearable wearable devices