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Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Author

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  • Manish Sharma

    (Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India)

  • Jainendra Tiwari

    (Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India)

  • U. Rajendra Acharya

    (School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
    Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
    School of Management and Enterprise, University of Southern Queensland, Springfield 4300, Australia)

Abstract

Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet’s cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen’s Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen’s Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.

Suggested Citation

  • Manish Sharma & Jainendra Tiwari & U. Rajendra Acharya, 2021. "Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals," IJERPH, MDPI, vol. 18(6), pages 1-29, March.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:6:p:3087-:d:518908
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    References listed on IDEAS

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    1. Jaypal Singh Rajput & Manish Sharma & U. Rajendra Acharya, 2019. "Hypertension Diagnosis Index for Discrimination of High-Risk Hypertension ECG Signals Using Optimal Orthogonal Wavelet Filter Bank," IJERPH, MDPI, vol. 16(21), pages 1-17, October.
    2. Ozal Yildirim & Ulas Baran Baloglu & U Rajendra Acharya, 2019. "A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals," IJERPH, MDPI, vol. 16(4), pages 1-21, February.
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    Citations

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    Cited by:

    1. Jaypal Singh Rajput & Manish Sharma & T. Sudheer Kumar & U. Rajendra Acharya, 2022. "Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals," IJERPH, MDPI, vol. 19(7), pages 1-16, March.
    2. Manish Sharma & Jaypal Singh Rajput & Ru San Tan & U. Rajendra Acharya, 2021. "Automated Detection of Hypertension Using Physiological Signals: A Review," IJERPH, MDPI, vol. 18(11), pages 1-26, May.

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