Hybrid manifold-deep convolutional neural network for sleep staging

• Published in: Methods (San Diego, Calif.) Vol. 202; pp. 164 - 172
• Main Authors: Zhang, Chuanhao, Liu, Sen, Han, Fang, Nie, Zedong, Lo, Benny, Zhang, Yuan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2022
• Subjects: Convolutional neural network; data collection; electroencephalography; hybrids; Hyperbolic attention; Manifold learning; neural networks; sleep; Sleep staging; Hyperbolic attention; Manifold learning; Convolutional neural network; Sleep staging
• ISSN: 1046-2023; 1095-9130; 1095-9130
• DOI: 10.1016/j.ymeth.2021.02.014

•A novel hybrid residual manifold convolutional neural network is proposed for sleep staging.•Mitigating the insufficient data problem by leveraging semi-supervised scheme.•Applying the channel attention mechanism for feature extraction. Analysis of electroencephalogram (EEG) is a crucial diagnostic criterion for many sleep disorders, of which sleep staging is an important component. Manual stage classification is a labor-intensive process and usually suffered from many subjective factors. Recently, more and more computer-aided techniques have been applied to this task, among which deep convolutional neural network has been performing well as an effective automatic classification model. Despite some comprehensive models have been developed to improve classification results, the accuracy for clinical applications has not been reached due to the lack of sufficient labeled data and the limitation of extracting latent discriminative EEG features. Therefore, we propose a novel hybrid manifold-deep convolutional neural network with hyperbolic attention. To overcome the shortage of labeled data, we update the semi-supervised training scheme as an optimal solution. In order to extract the latent feature representation, we introduce the manifold learning module and the hyperbolic module to extract more discriminative information. Eight subjects from the public dataset are utilized to evaluate our pipeline, and the model achieved 89% accuracy, 70% precision, 80% sensitivity, 72% f1-score and kappa coefficient of 78%, respectively. The proposed model demonstrates powerful ability in extracting feature representation and achieves promising results by using semi-supervised training scheme. Therefore, our approach shows strong potential for future clinical development.