Sleep microstructure dynamics and neurocognitive performance in obstructive sleep apnea syndrome patients.

The present study examined the relationship between the increment in cyclic alternating patterns (CAPs) in sleep electroencephalography and neurocognitive decline in obstructive Sleep Apnea Syndrome (OSAS) patients through source localization of the phase-A of CAPs. All-night polysomnographic recordings of 10 OSAS patients and 4 control subjects along with their cognitive profile using the Addenbrooke's Cognitive Examination (ACE) test were acquired. The neuropsychological assessment involved five key domains including attention and orientation, verbal fluency, memory, language and visuo-spatial skills. The standardized low-resolution brain electromagnetic tomography (sLORETA) tool was used to source-localize the phase-A of CAPs in sleep EEG aiming to investigate the correlation between CAP phase-A and cognitive functions. Our findings suggested a significant increase in CAP rates among OSAS subjects versus control subjects. Moreover, sLORETA revealed that CAP phase-A is mostly activated in frontoparietal cortices. As CAP rate increases, the activity of phase-A in such areas is dramatically enhanced leading to arousal instability, lower sleep efficiency and a possibly impaired cortical capacity to consolidate cognitive inputs in frontal and parietal areas during sleep. As such, cognitive domains including verbal fluency, memory and visuo-spatial skills which predominantly relate to frontoparietal areas tend to be affected. Based on our findings, CAP activity may possibly be considered as a predictor of cognitive decline among OSAS patients.

A Distributed Classification Procedure for Automatic Sleep Stage Scoring Based on Instantaneous Electroencephalogram Phase and Envelope Features.

During the past decades, a great body of research has been devoted to automatic sleep stage scoring using the electroencephalogram (EEG). However, the results are not yet satisfactory to be used as a standard procedure in clinical studies. In this study, using recent developments in robust EEG phase extraction, a novel set of EEG-based features containing the Shannon entropy of the instantaneous analytical form envelope and frequencies of the EEG are proposed for sleep stage scoring. The proposed feature set is used to construct a distributed decision-tree classifier, with binary K-nearest neighbor classifiers at each decision node. The decision-tree structure is designed by brute-force-search over various combinations of the proposed feature set. The performance of the proposed approach is evaluated over two available sleep EEG data sets acquired using single-channel EEG. The first set contains 20 healthy young subjects containing equal number of male and female, and the second one has been acquired from 140 adult subjects from both genders, with sleep disorder. The performance of the proposed method is tested versus state-of-the-art classifiers. The results demonstrate that the proposed method, resulted in overall accuracies of 88.97% and 83.17% over the two data sets, respectively. Considering the high performance and simplicity of the proposed scheme, the method can be of interest for clinical sleep disorder studies.

A comparative review on sleep stage classification methods in patients and healthy individuals.

BACKGROUND AND OBJECTIVE: Proper scoring of sleep stages can give clinical information on diagnosing patients with sleep disorders. Since traditional visual scoring of the entire sleep is highly time-consuming and dependent to experts' experience, automatic schemes based on electroencephalogram (EEG) analysis are broadly developed to solve these problems. This review presents an overview on the most suitable methods in terms of preprocessing, feature extraction, feature selection and classifier adopted to precisely discriminate the sleep stages. METHODS: This study round up a wide range of research findings concerning the application of the sleep stage classification. The fundamental qualitative methods along with the state-of-the-art quantitative techniques for sleep stage scoring are comprehensively introduced. Moreover, according to the results of the investigated studies, five research papers are chosen and practically implemented on a well-known public available sleep EEG dataset. They are applied to single-channel EEG of 40 subjects containing equal number of healthy and patient individuals. Feature extraction and classification schemes are assessed in terms of accuracy and robustness against noise. Furthermore, an additional implementation phase is added to this research in which all combinations of the implemented features and classifiers are considered to find the best combination for sleep analysis. RESULTS: According to our achieved results on both groups, entropy of wavelet coefficients along with random forest classifier are chosen as the best feature and classifier, respectively. The mentioned feature and classifier provide 87.06% accuracy on healthy subjects and 69.05% on patient group. CONCLUSIONS: In this paper, the road map of EEG-base sleep stage scoring methods is clearly sketched. Implementing the state-of-the-art methods and even their combination on both healthy and patient datasets indicates that although the accuracy on healthy subjects are remarkable, the results for the main community (patient group) by the quantitative methods are not promising yet. The reasons rise from adopting non-matched sleep EEG features from other signal processing fields such as communication. As a conclusion, developing sleep pattern-related features deem necessary to enhance the performance of this process.