脑电图
多导睡眠图
嗜睡症
睡眠阶段
睡眠障碍
失眠症
睡眠(系统调用)
随机森林
听力学
计算机科学
心理学
人工智能
医学
神经科学
精神科
神经学
操作系统
作者
Stavros I. Dimitriadis,Christos Salis,Dimitris Liparas
出处
期刊:Journal of Neural Engineering
[IOP Publishing]
日期:2021-05-24
卷期号:18 (4): 046064-046064
被引量:12
标识
DOI:10.1088/1741-2552/abf773
摘要
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 RFCFCmodel 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI