The effect of temazepam on assessment of severity of obstructive sleep apnea by polysomnography

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

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.

Dental sleep medicine: Time to incorporate sleep apnoea education in the dental curriculum

Dental sleep medicine is a discipline that includes conditions such as sleep breathing disorders (eg snoring and sleep apnoea), sleep bruxism, orofacial pain and sleep-related complaints, and to some extent gastro-oesophageal reflux disorder and/or insomnia. Obstructive sleep apnoea (OSA) is a life-threatening condition that dentists need to identify and manage when indicated in order to increase patient well-being and to be taken in consideration in the dental curriculum. The main objective of this paper is to highlight the relevance of dental sleep medicine in the context of dental education, and to discuss potential educational content for integration in the dental curriculum with a focus on OSA, a condition that is not yet integrated in many dental training curricula around the world.