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An automatic sleep disorder detection based on EEG cross-frequency coupling and random forest model

Dimitriadis, Stavros I., Salis, Christos I. and Liparas, Dimitris 2021. An automatic sleep disorder detection based on EEG cross-frequency coupling and random forest model. Journal of Neural Engineering 18 (4) , 046064. 10.1088/1741-2552/abf773

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Abstract

Objective. Sleep disorders are medical disorders of a subject's sleep architecture and based on their severity, they can interfere with mental, emotional and physical functioning. The most common ones are insomnia, narcolepsy, sleep apnea, bruxism, etc. There is an increased risk of developing sleep disorders in elderly like insomnia, periodic leg movements, rapid eye movement behavior disorders, sleep disorder breathing, etc. Consequently, their accurate diagnosis and classification are important steps towards an early stage treatment that could save the life of a patient. Approach. The electroencephalographic (EEG) signal is the most sensitive and important biosignal, which is able to capture the brain sleep activity that is sensitive to sleep. In this study, we attempt to analyze EEG sleep activity via complementary cross-frequency coupling (CFC) estimates, which further feed a classifier, aiming to discriminate sleep disorders. We adopted an open EEG database with recordings that were grouped into seven sleep disorders and a healthy control. The EEG brain activity from common sensors has been analyzed with two basic types of CFC. Main results. Finally, a random forest (RF) classification model was built on CFC patterns, which were extracted from non-cyclic alternating pattern epochs. Our RFCFC model achieved a 74% multiclass accuracy. Both types of CFC, phase-to-amplitude and amplitude–amplitude coupling patterns contribute to the accuracy of the RF model, thus supporting their complementary information. Significance. CFC patterns, in conjunction with the RF classifier proved a valuable biomarker for the classification of sleep disorders.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information: Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license
Publisher: IOP Publishing
ISSN: 1741-2560
Date of First Compliant Deposit: 13 April 2021
Date of Acceptance: 13 April 2021
Last Modified: 02 Jun 2021 12:46
URI: https://orca.cardiff.ac.uk/id/eprint/140448

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