An automatic sleep disorder detection based on EEG cross-frequency coupling and random forest model.
Stavros I DimitriadisChristos I SalisDimitris LiparasPublished in: Journal of neural engineering (2021)
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.
Keyphrases
- sleep quality
- physical activity
- resting state
- functional connectivity
- early stage
- machine learning
- deep learning
- working memory
- sleep apnea
- healthcare
- depressive symptoms
- climate change
- squamous cell carcinoma
- radiation therapy
- lymph node
- ionic liquid
- sentinel lymph node
- loop mediated isothermal amplification
- sensitive detection
- neoadjuvant chemotherapy
- electronic health record
- quantum dots