Study on the classification of sleep stages in EEG signals based on DoubleLinkSleepCLNet

• Published in: Sleep & breathing Vol. 28; no. 5; pp. 2055 - 2061
• Main Authors: Ma, Xiaoxiao, Yin, Guimei, Wang, Lin, Shi, Dongli, Zhao, Yanli, Tan, Shuping, Yin, Mengzhen, Zhao, Jianghao, Wang, Maoyun, Chen, Yanjun
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2024; Springer Nature B.V
• Subjects: Accuracy; Adult; Classification; Deep Learning; Dentistry; EEG; Electroencephalography; Humans; Information processing; Internal Medicine; Long short-term memory; Male; Medicine; Medicine & Public Health; Neural networks; Neural Networks, Computer; Neurology; Otorhinolaryngology; Pediatrics; Pneumology/Respiratory System; Polysomnography; Signal Processing, Computer-Assisted; Sleep; Sleep Breathing Physiology and Disorders • Original Article; Sleep Stages - physiology; Deep learning; Hilbert transform; Sleep stage; EEG
• ISSN: 1520-9512; 1522-1709; 1522-1709
• DOI: 10.1007/s11325-024-03112-2

Purpose The classification of sleep stages based on Electroencephalogram (EEG) changes has significant implications for evaluating sleep quality and sleep status. Most polysomnography (PSG) systems have a limited number of channels and do not achieve optimal classification performance due to a paucity of raw data. To leverage the data characteristics and enhance the classification accuracy, we propose and evaluate a novel dual-link deep neural network model, ‘DoubleLinkSleepCLNet’. Methods The DoubleLinkSleepCLNet model performs feature extraction and efficient classification on both the raw EEG and the EEG processed with the Hilbert transform. It leverages the frequency domain and time domain feature modules, resulting in superior performance compared to other models. Results The DoubleLinkSleepCLNet model, using the 2 Raw/2 Hilbert data modes, achieved the highest classification performance with an accuracy of 88.47%. The average accuracy of the EEG was improved by approximately 4.08% after the application of the Hilbert transform. Additionally, Convolutional Neural Network (CNN) demonstrated superior performance in processing phase information, whereas Long Short-Term Memory (LSTM) excelled in handling time series data. Conclusion The application of the Hilbert transform to EEG data, followed by processing it with a convolutional neural network, enhances the accuracy of the model. These findings introduce novel concepts for accelerating sleep stage prediction research, suggesting potential applications of these methods to other EEG analyses.