Single-channel EEG sleep staging based on data augmentation and cross-subject discrepancy alleviation

• Published in: Computers in biology and medicine Vol. 149; p. 106044
• Main Authors: He, Zhengling, Du, Lidong, Wang, Peng, Xia, Pan, Liu, Zhe, Song, Yuanlin, Chen, Xianxiang, Fang, Zhen
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2022; Elsevier Limited
• Subjects: Accuracy; Adaptation; Algorithms; Classification; Data augmentation; Datasets; Deep learning; Domain adaptation; Domains; EEG; Electroencephalography; Electroencephalography - methods; Feature maps; Feature selection; Human error; Humans; Inspection; Internal Medicine; Machine Learning; Methods; Neural networks; Neural Networks, Computer; Other; Performance enhancement; Performance evaluation; Single-channel electroencephalogram (EEG); Sleep; Sleep disorders; Sleep stage classification; Sleep Stages; Support vector machines; Transfer attention; Visual fields; Transfer attention; Domain adaptation; Sleep stage classification; Single-channel electroencephalogram (EEG); Data augmentation

Automatic sleep stage classification is an effective technology compared to conventional artificial visual inspection in the field of sleep staging. Numerous algorithms based on machine learning and deep learning on single-channel electroencephalogram (EEG) have been proposed in recent years, howeve...