Abstract
Monitoring single-channel EEG is a promising home-based approach for insomnia identification. Currently, many automatic sleep stage scoring approaches based on single-channel EEG have been developed, whereas few studies research on automatic insomnia identification based on single-channel EEG labelled with sleep stage annotations. In this paper, we propose a one-dimensional convolutional neural network (1D-CNN) model for automatic insomnia identification based on single-channel EEG labelled with sleep stage annotations, and further investigate the identification performance based on different sleep stages EEG epochs. Single-channel EEG on 9 insomnia patients and 9 healthy subjects was used in this study. We constructed 4 subdatasets from EEG epochs based on the sleep stage annotations: All sleep stage dataset (ALL-DS), REM sleep stage dataset (REM-DS), light sleep stage dataset (LSS-DS), and SWS sleep stage dataset (SWS-DS). Subsequently, 4 subdatasets were fed into our 1D-CNN. We conducted experiments under intra-patient and inter-patient paradigms, respectively. Our experiments demonstrated that our 1D-CNN leveraging 3 subdatasets composed of REM, LSS and SWS epochs, respectively, achieved higher average accuracies in comparison with baseline methods under both intra-patient and inter-patient paradigms. The experimental results also indicated that amongst all the sleep stages, 1D-CNN leveraging REM and SWS epochs exhibited the best insomnia identification average accuracies in intra-patient paradigm, which are 98.98% and 99.16% respectively, whereas no statistically significant difference was found in inter-patient paradigm. For automatic insomnia identification based on single-channel EEG labelled with sleep stage annotations, 1D-CNN model introduced in this paper could achieve superior performance than traditional methods.
Highlights
Sleep is a fundamental physiological activity, which plays a crucial role in physical and mental health for human body [1]
We propose a 1D-Convolutional neural networks (CNN) model for automatic insomnia identification leveraging single-channel EEG labelled with sleep stage annotations, and further investigate the identification performance based on different sleep stages
Our experiments demonstrated that our 1D-CNN leveraging the 3 subdatasets composed of rapid eye movement (REM), light sleep stage (LSS) and slow wave sleep (SWS) epochs, respectively, achieved higher average accuracies in comparison with baseline methods both in intra-patient and inter-patient experiments
Summary
Sleep is a fundamental physiological activity, which plays a crucial role in physical and mental health for human body [1]. Shahin et al [14] extracted statistical, temporal and spectral features of EEG signals, and leveraged deep neural network (DNN) for automatic insomnia identification. Since most of the existing work for automatic insomnia identification task was based on hand-crafted features and traditional machine learning algorithms, an end-to-end one-dimensional CNN (1D-CNN) model based on single-channel EEG signal is investigated in this study. We propose a 1D-CNN model for automatic insomnia identification leveraging single-channel EEG labelled with sleep stage annotations, and further investigate the identification performance based on different sleep stages. This is the first implementation of CNN in automatic insomnia identification task to the best of the author’s knowledge.
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