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Appl. Sci. 2018, 8(5), 697;

Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague, Czech Republic
Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic
Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Prague, Czech Republic
Author to whom correspondence should be addressed.
Received: 2 April 2018 / Revised: 26 April 2018 / Accepted: 27 April 2018 / Published: 1 May 2018
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Multimodal signal analysis based on sophisticated sensors, efficient communication systems and fast parallel processing methods has a rapidly increasing range of multidisciplinary applications. The present paper is devoted to pattern recognition, machine learning, and the analysis of sleep stages in the detection of sleep disorders using polysomnography (PSG) data, including electroencephalography (EEG), breathing (Flow), and electro-oculogram (EOG) signals. The proposed method is based on the classification of selected features by a neural network system with sigmoidal and softmax transfer functions using Bayesian methods for the evaluation of the probabilities of the separate classes. The application is devoted to the analysis of the sleep stages of 184 individuals with different diagnoses, using EEG and further PSG signals. Data analysis points to an average increase of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of the Rapid Eye Movement (REM) stages by 0.8% per 10 years. The mean classification accuracy for given sets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1) and 89.6% (STD:1.9), respectively. The proposed methods enable the use of adaptive learning processes for the detection and classification of health disorders based on prior specialist experience and man–machine interaction. View Full-Text
Keywords: multimodal signal analysis; pattern recognition; machine learning; computational intelligence; polysomnography; sleep stage classification multimodal signal analysis; pattern recognition; machine learning; computational intelligence; polysomnography; sleep stage classification

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Procházka, A.; Kuchyňka, J.; Vyšata, O.; Cejnar, P.; Vališ, M.; Mařík, V. Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition. Appl. Sci. 2018, 8, 697.

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