Abstract
The classification of sleep stages is the process which helps to evaluate the quality of sleep and detect the sleep related disorders. Through analyzing the electroencephalography, the sleep stages can be discriminated manually by specialists. However, this can be a laboriousness work because of the huge datasets. Until now, several studies have been conducted based on the automatic analysis of electroencephalography. Still, as the development of wearable technology, there is a need for an accurate and single-channel electroencephalography based sleep stages identification system. In this paper, a state-space based sleep stages classification method is proposed using the proposed model based essence features extraction method. This method employed the state-space model to establish the intrinsic models based on the single-channel electroencephalography, from which the features used for further classification can be extracted. For 2-stage to 6-stage classification of sleep states, the verification system can achieve 98.6%, 94.9%, 93.0%, 92.3%, 91.8% accuracy on the Sleep-EDF database, and also reach 94.9%, 87.7%, 82.7%, 80.9%, 78.2% on Dreams Subjects database.
Highlights
Sleep is a naturally recurring state of mind and body, in which the body alternates between rapid eye movement (REM) sleep and non-REM (NREM) sleep, and sleep quality [1]– [5] is one of the important factors affecting the human being’s health
According to the Rechtsaffen and Kales (R&K) [6] criteria proposed in 1968, the sleep of a healthy human being can be divided into six sleep stages including wakefulness (W), NREM sleep stage1 (S1), NREM sleep stage2 (S2), NREM sleep stage3 (S3), NREM sleep stage4 (S4) and rapid eye movement (REM)
The model based essence features (MBEFs) will be extracted from the estimated models
Summary
Sleep is a naturally recurring state of mind and body, in which the body alternates between rapid eye movement (REM) sleep and non-REM (NREM) sleep, and sleep quality [1]– [5] is one of the important factors affecting the human being’s health. Sharma et al [17] developed a single-channel EEG based sleep-stages classification system using the time-frequency features extracted from the EEG datasets. This method achieved classification accuracies of 98.3%, 93.9%, 92.1%, 91.7%, and 91.5% for 2-stage to 6-stage on 85900 epochs which belong to the Sleep-EDF database. Liang et al [25] proposed an automatic sleep-scoring method with multiscale entropy(MSE) They achieved accuracy 83.6% for 5 stages on 3708 epochs from 20 healthy individuals. Presented is a state-space model (SSM) based sleep stages classification method, through which the accurate assessment of the sleep activity can be obtained by singlechannel EEG datasets.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
