Multiclass Arrhythmia Detection and Classification From Photoplethysmography Signals Using a Deep Convolutional Neural Network

• Published in: Journal of the American Heart Association Vol. 11; no. 7; p. e023555
• Main Authors: Liu, Zengding, Zhou, Bin, Jiang, Zhiming, Chen, Xi, Li, Ye, Tang, Min, Miao, Fen
• Format: Journal Article
• Language: English
• Published: England John Wiley and Sons Inc 05.04.2022; Wiley
• Subjects: Algorithms; arrhythmias; Atrial Fibrillation - diagnosis; deep convolutional neural networks; Electrocardiography - methods; Humans; Neural Networks, Computer; Original Research; photoplethysmography; Photoplethysmography - methods; Ventricular Premature Complexes; arrhythmias; photoplethysmography; deep convolutional neural networks

Background Studies have reported the use of photoplethysmography signals to detect atrial fibrillation; however, the use of photoplethysmography signals in classifying multiclass arrhythmias has rarely been reported. Our study investigated the feasibility of using photoplethysmography signals and a...