A comparative review on sleep stage classification methods in patients and healthy individuals. (March 2017)

• Record Type: Journal Article
• Authors: Boostani, Reza; Karimzadeh, Foroozan; Nami, Mohammad
• Is Part Of: Computer methods and programs in biomedicine. Volume 140(2017)
• Page Start: 77
• Page End: 91
• Publication Date: 2017-03
• Subjects: Sleep stage classification -- Wavelet transform -- Random forest classifier -- Entropy; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.12.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This is the multi-aspect review article in which most of the qualitative and quantitative methods for sleep stage scoring have been investigated. In addition we demonstrate the statistics indicating the rate of publications in this field based on both year and the journal where they were published. We do a fair comparison between five state-of-the-art methods by applying them to 2 different datasets including healthy and patient subjects. In addition in a combinatorial phase, the combination of various feature sets and classifier are investigated and the most suitable one is reported. This review article includes a general overview and introduction with enough basic information to make the article informative for non-specialist scientists. Moreover, it gives a broad perspective and demonstrates a road map to whom wants to start a research on the related topics. Therefore, due to its training aspect, it paves the way for beginners and also specialists in this field and consequently, the rate of the readers will increase. Due to the importance of sleep stage scoring for diagnosing sleep disorders and the direct relation of sleep medicine to the human health and also the limited number of review paper in this field, this comparative review for both healthy and patient subjects is deemed to be crucial. Abstract: Background and objective: Proper scoring of sleep stages can give clinical information on diagnosing patients with sleep disorders. Since traditional visual scoring of the entire sleep is highly time-consuming and dependent to experts' experience, automatic schemes based on electroencephalogram (EEG) analysis are broadly developed to solve these problems. This review presents an overview on the most suitable methods in terms of preprocessing, feature extraction, feature selection and classifier adopted to precisely discriminate the sleep stages. Methods: This study round up a wide range of research findings concerning the application of the sleep stage classification. The fundamental qualitative methods along with the state-of-the-art quantitative techniques for sleep stage scoring are comprehensively introduced. Moreover, according to the results of the investigated studies, five research papers are chosen and practically implemented on a well-known public available sleep EEG dataset. They are applied to single-channel EEG of 40 subjects containing equal number of healthy and patient individuals. Feature extraction and classification schemes are assessed in terms of accuracy and robustness against noise. Furthermore, an additional implementation phase is added to this research in which all combinations of the implemented features and classifiers are considered to find the best combination for sleep analysis. Results: According to our achieved results on both groups, entropy of wavelet coefficients along with random forest classifier are chosen as the best feature and classifier, respectively. The mentioned feature and classifier provide 87.06% accuracy on healthy subjects and 69.05% on patient group. Conclusions: In this paper, the road map of EEG-base sleep stage scoring methods is clearly sketched. Implementing the state-of-the-art methods and even their combination on both healthy and patient datasets indicates that although the accuracy on healthy subjects are remarkable, the results for the main community (patient group) by the quantitative methods are not promising yet. The reasons rise from adopting non-matched sleep EEG features from other signal processing fields such as communication. As a conclusion, developing sleep pattern-related features deem necessary to enhance the performance of this process.