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
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3457969

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 EEG data. This algorithm employed a one-dimensional convolutional neural network (1D-CNN) to extract temporal features from raw EEG, and a two-dimensional CNN (2D-CNN) to extract time-frequency features from spectrograms generated through continuous wavelet transform (CWT) at the epoch level. These features were subsequently fused and further fed into a temporal convolutional network (TCN) to classify sleep stages at the sequence level. Moreover, a two-step training strategy was used to enhance the model's performance on an imbalanced dataset. Our proposed method exhibits superior performance in the 5-class classification task for healthy subjects, as evaluated on the SHHS-1, Sleep-EDF-153, and ISRUC-S1 datasets. This work provided a straightforward and promising method for improving the accuracy of automatic sleep staging using only single-channel EEG, and the proposed method exhibited great potential for future applications in professional sleep monitoring, which could effectively alleviate the workload of sleep technicians.