A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning

• Published in: Computational intelligence and neuroscience 2023, Vol.2023 (1), p.9379618-9379618
• Main Authors: Badiei, Afsoon, Meshgini, Saeed, Rezaee, Khosro
• Format: Article
• Language: English
• Subjects: Algorithms; Alzheimer's disease; Analysis; Arousal; Automation; Blood pressure; Cardiovascular disease; Cardiovascular diseases; Classification; Clinical trials; Cognitive ability; Deep learning; Detectors; EEG; EKG; Electrocardiogram; Electrocardiography; Electroencephalography; Electroencephalography - methods; Electromyography; Fractal transforms; Fractals; Heart rate; Heuristic methods; Humans; Learning; Liapunov exponents; Neural networks; Neurophysiology; Optimization; Physiological aspects; Physiology; Polysomnography - methods; Radial basis function; Sexual disorders; Sleep; Sleep - physiology; Sleep Arousal Disorders; Sleep disorders; Sleep Wake Disorders - diagnosis; Support vector machines; Teaching methods; Wavelet transforms
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2023/9379618

The vast majority of sleep disturbances are caused by various types of sleep arousal. To diagnose sleep disorders and prevent health problems such as cardiovascular disease and cognitive impairment, sleep arousals must be accurately detected. Consequently, sleep specialists must spend considerable time and effort analyzing polysomnography (PSG) recordings to determine the level of arousal during sleep. The development of an automated sleep arousal detection system based on PSG would considerably benefit clinicians. We quantify the EEG-ECG by using Lyapunov exponents, fractals, and wavelet transforms to identify sleep stages and arousal disorders. In this paper, an efficient hybrid-learning method is introduced for the first time to detect and assess arousal incidents. Modified drone squadron optimization (mDSO) algorithm is used to optimize the support vector machine (SVM) with radial basis function (RBF) kernel. EEG-ECG signals are preprocessed samples from the SHHS sleep dataset and the PhysioBank challenge 2018. In comparison to other traditional methods for identifying sleep disorders, our physiological signals correlation innovation is much better than similar approaches. Based on the proposed model, the average error rate was less than 2%–7%, respectively, for two-class and four-class issues. Additionally, the proper classification of the five sleep stages is determined to be accurate 92.3% of the time. In clinical trials of sleep disorders, the hybrid-learning model technique based on EEG-ECG signal correlation features is effective in detecting arousals.