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

• Published in: Journal of neural engineering Vol. 18; no. 4; pp. 46064 - 46077
• Main Authors: Dimitriadis, Stavros I, Salis, Christos I, Liparas, Dimitris
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 24.05.2021
• Subjects: Aged; classification; cross-frequency coupling; Electroencephalography; Humans; random forest; Sleep; Sleep Apnea Syndromes; sleep disorders; Sleep Stages; Sleep Wake Disorders - diagnosis; electroencephalography; sleep disorders; random forest; classification; cross-frequency coupling
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/abf773

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 RF CFC 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.