Heightened Background Cortical Synchrony in Patients With Epilepsy: EEG Phase Synchrony Analysis During Awake and Sleep Stages Using Novel Ensemble Measure

Sleep quality and architecture in Idiopathic generalized epilepsy: A systematic review and meta-analysis

Idiopathic generalized epilepsies are a group of sleep related epilepsy syndromes with sleep deprivation as a strong trigger for seizures and increased spike-wave activity during sleep and transition to sleep. Neuropsychological deficits are common in Idiopathic generalized epilepsy patients. Learning and memory processes are closely linked to sleep. Therefore, this systematic review and meta-analysis investigates the evidence of sleep disturbances in Idiopathic generalized epilepsy patients. A search of the databases EMBASE, Medline and Scopus identified 22 studies comparing polysomnographic parameters and scores of sleep questionnaires between Idiopathic generalized epilepsy patients and healthy controls. Random effect univariate meta-analyses revealed reduced sleep efficiency, total sleep time, proportion of N2 stage and prolonged REM onset latency in Idiopathic generalized epilepsy patients. Self-assessed sleep quality of patients measured by the Pittsburgh sleep quality index was lower in two thirds of reporting studies. Considering the influence on behavioral issues, cognitive performance and quality of life, the revealed alteration in sleep architecture and lower subjective sleep quality emphasizes the importance of screening for sleep disturbances in the medical care of patients with Idiopathic generalized epilepsy.

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

The aim of this study was to develop an integrated system of non-contact sleep stage detection and sleep disorder treatment for health monitoring. Hence, a method of brain activity detection based on microwave scattering technology instead of scalp electroencephalogram was developed to evaluate the sleep stage. First, microwaves at a specific frequency were used to penetrate the functional sites of the brain in patients with sleep disorders to change the firing frequency of the activated areas of the brain and analyze and evaluate statistically the effects on sleep improvement. Then, a wavelet packet algorithm was used to decompose the microwave transmission signal, the refined composite multiscale sample entropy, the refined composite multiscale fluctuation-based dispersion entropy and multivariate multiscale weighted permutation entropy were obtained as features from the wavelet packet coefficient. Finally, the mutual information-principal component analysis feature selection method was used to optimize the feature set and random forest was used to classify and evaluate the sleep stage. The results show that after four times of microwave modulation treatment, sleep efficiency improved continuously, the overall maintenance was above 80%, and the insomnia rate was reduced gradually. The overall classification accuracy of the four sleep stages was 86.4%. The results indicate that the microwaves with a certain frequency can treat sleep disorders and detect abnormal brain activity. Therefore, the microwave scattering method is of great significance in the development of a new brain disease treatment, diagnosis and clinical application system.