Classification and transfer learning of sleep spindles based on convolutional neural networks

• Published in: Frontiers in neuroscience Vol. 18; p. 1396917
• Main Authors: Liang, Jun, Belkacem, Abdelkader Nasreddine, Song, Yanxin, Wang, Jiaxin, Ai, Zhiguo, Wang, Xuanqi, Guo, Jun, Fan, Lingfeng, Wang, Changming, Ji, Bowen, Wang, Zengguang
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 24.04.2024
• Subjects: convolutional neural network; electroencephalogram; Neuroscience; polysomnography; sleep spindles; transfer learning; transfer learning; sleep spindles; polysomnography; convolutional neural network; electroencephalogram
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2024.1396917

Sleep plays a critical role in human physiological and psychological health, and electroencephalography (EEG), an effective sleep-monitoring method, is of great importance in revealing sleep characteristics and aiding the diagnosis of sleep disorders. Sleep spindles, which are a typical phenomenon in EEG, hold importance in sleep science. This paper proposes a novel convolutional neural network (CNN) model to classify sleep spindles. Transfer learning is employed to apply the model trained on the sleep spindles of healthy subjects to those of subjects with insomnia for classification. To analyze the effect of transfer learning, we discuss the classification results of both partially and fully transferred convolutional layers. The classification accuracy for the healthy and insomnia subjects' spindles were 93.68% and 92.77%, respectively. During transfer learning, when transferring all convolutional layers, the classification accuracy for the insomnia subjects' spindles was 91.41% and transferring only the first four convolutional layers achieved a classification result of 92.80%. The experimental results demonstrate that the proposed CNN model can effectively classify sleep spindles. Furthermore, the features learned from the data of the normal subjects can be effectively applied to the data for subjects with insomnia, yielding desirable outcomes. These outcomes underscore the efficacy of both the collected dataset and the proposed CNN model. The proposed model exhibits potential as a rapid and effective means to diagnose and treat sleep disorders, thereby improving the speed and quality of patient care.