Study on the classification of sleep stages in EEG signals based on DoubleLinkSleepCLNet

• Published in: Sleep & breathing Vol. 28; no. 5; pp. 2055 - 2061
• Main Authors: Ma, Xiaoxiao, Yin, Guimei, Wang, Lin, Shi, Dongli, Zhao, Yanli, Tan, Shuping, Yin, Mengzhen, Zhao, Jianghao, Wang, Maoyun, Chen, Yanjun
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2024; Springer Nature B.V
• Subjects: Accuracy; Adult; Classification; Deep Learning; Dentistry; EEG; Electroencephalography; Humans; Information processing; Internal Medicine; Long short-term memory; Male; Medicine; Medicine & Public Health; Neural networks; Neural Networks, Computer; Neurology; Otorhinolaryngology; Pediatrics; Pneumology/Respiratory System; Polysomnography; Signal Processing, Computer-Assisted; Sleep; Sleep Breathing Physiology and Disorders • Original Article; Sleep Stages - physiology; Deep learning; Hilbert transform; Sleep stage; EEG

Purpose The classification of sleep stages based on Electroencephalogram (EEG) changes has significant implications for evaluating sleep quality and sleep status. Most polysomnography (PSG) systems have a limited number of channels and do not achieve optimal classification performance due to a pauci...