Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data

• Published in: Sleep & breathing Vol. 24; no. 2; pp. 581 - 590
• Main Authors: Zhang, Xiaoqing, Xu, Mingkai, Li, Yanru, Su, Minmin, Xu, Ziyao, Wang, Chunyan, Kang, Dan, Li, Hongguang, Mu, Xin, Ding, Xiu, Xu, Wen, Wang, Xingjun, Han, Demin
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2020; Springer Nature B.V
• Subjects: Accuracy; Adult; Aged; Automation; Classification; Datasets; Deep Learning; Dentistry; Female; Humans; Internal Medicine; Male; Medicine; Medicine & Public Health; Middle Aged; Neural networks; Neural Networks, Computer; Neurology; Otorhinolaryngology; Pediatrics; Pneumology/Respiratory System; Polysomnography; Sleep; Sleep Apnea, Obstructive - physiopathology; Sleep Breathing Physiology and Disorders • Original; Sleep Breathing Physiology and Disorders • Original Article; Sleep Stages - physiology; Young Adult; Deep learning; Polysomnography (PSG); Obstructive sleep apnea (OSA); Sleep staging

Purpose To develop an automated framework for sleep stage scoring from PSG via a deep neural network. Methods An automated deep neural network was proposed by using a multi-model integration strategy with multiple signal channels as input. All of the data were collected from one single medical cente...