A Feature Fusion Model Based on Temporal Convolutional Network for Automatic Sleep Staging Using Single-Channel EEG

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 11; pp. 6641 - 6652
• Main Authors: Bao, Jiameng, Wang, Guangming, Wang, Tianyu, Wu, Ning, Hu, Shimin, Lee, Won Hee, Lo, Sio-Long, Yan, Xiangguo, Zheng, Yang, Wang, Gang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2024
• Subjects: Adult; Algorithms; Brain modeling; Continuous wavelet transforms; Convolutional neural networks; Deep Learning; EEG; Electroencephalography; Electroencephalography - methods; Feature extraction; feature fusion; Female; Humans; Male; Neural Networks, Computer; Signal Processing, Computer-Assisted; Sleep; Sleep Stages - physiology; sleep staging; tempo- ral convolutional network; Time-frequency analysis; Wavelet Analysis; Young Adult

Sleep staging is a crucial task in sleep monitoring and diagnosis, but clinical sleep staging is both time-consuming and subjective. In this study, we proposed a novel deep learning algorithm named feature fusion temporal convolutional network (FFTCN) for automatic sleep staging using single-channel...