Detection of insomnia using advanced complexity and entropy features of sleep stage data of EEG recordings

Abstract

Sleep disorders affect the physical ability in the function under mental, emotional, and physical forms depending upon its intensity. These abnormalities are observed on the basis of structure of sleep. Most prevalent sleep disorders are insomnia, bruxism, depression, and narcolepsy. Common issues in sleep disorder are sleeplessness, irregular leg movements, problems with fast eye movement behavior and breathing abnormalities. Therefore, an early-stage therapy that might save a patient's life depends on a precise diagnosis and categorization. The much more sensitive as well as significant bio-signal is electroencephalographic (EEG) signal. It has capacity to record sleep-sensitive brain activity. We used an available EEG database which had recordings divided into different types of sleep disturbances as well as a healthy control group. Popular sensor's EEG brain function has been examined. Ultimately, using patterns taken from EEG data, a categorization AI model was created. Extracted characteristics worked well as a biomarker for identifying sleep problems when combined with an AI classifier.

Keywords

• Sleep disorder
• Spectral entropy
• Kolmogorov complexity
• Insomnia
• EEG