Complexity science for sleep stage classification from EEG

Automatic sleep stage classification is an important paradigm in computational intelligence and promises consider- able advantages to the health care. Most current automated methods require the multiple electroencephalogram (EEG) chan- nels and typically cannot distinguish the S1 sleep stage from EEG. The aim of this study is to revisit automatic sleep stage classification from EEGs using complexity science methods. The proposed method applies fuzzy entropy and permutation entropy as kernels of multi-scale entropy analysis. To account for sleep transition, the preceding and following 30 seconds of epoch data were used for analysis as well as the current epoch. Combining the entropy and spectral edge frequency features extracted from one EEG channel, a multi-class support vector machine (SVM) was able to classify 93.8% of 5 sleep stages for the SleepEDF database [expanded], with the sensitivity of S1 stage was 49.1%. Also, the Kappa’s coefficient yielded 0.90, which indicates almost perfect agreement

An adaptive filtering approach using supervised SSA for identification of sleep stages from EEG

Purpose: Sleep is a complex physiological state and an indicator of the changes in the brain function similar to those occurring in many psychiatric and neurological conditions. Since visual sleep scoring consuming process, automatic sleep staging methods, also called scoring, hold promise in diagnosing alterations in the sleep process and the sleep EEG more effectively. Method: In this paper, a supervised approach for sleep scoring from single channel EEG signals is proposed. First, a supervised singular spectrum analysis (SSA) which is a subspace based method is used to extract the desired signal for each stage of sleep. Then, two recursive least squares (RLS) adaptive filters are trained and used to identify first and deep sleep stages. Result: The proposed system which can be considered as a filter bank for separating multiple signal subbands is tested using real EEG where the results verify the accuracy of the proposed method. Conclusion: The overall result show the effectiveness of algorithm for detection of sleep stages from EEG signals often characterised by a sharp increase in delta and a rapid decrease in alpha as sleep deepens

A continuous mapping of sleep states through association of EEG with a mesoscale cortical model

Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time

Genetics of human sleep EEG

Sleep characteristics are candidates for predictive biological markers in patients with severe psychiatric diseases, in particular affective disorder and schizophrenia. The genetic components of sleep determination in humans remain, to a large degree, unelucidated. In particular, the heritability of rapid eye movement (REM) sleep and EEG bursts of oscillatory brain activity in Non-REM sleep, i.e. sleep spindles, are of interest. In addition, recent findings suggest a strong role of distinct sleep spindle types in memory consolidation, making it important to identify sleep spindles in slow wave sleep (SWS) and to separate slow and fast spindle localization in the frequency range. However, predictive sleep biomarker research requires large sample sizes of healthy and affected human individuals. Therefore, the present work addressed two questions. The first aim was to optimize data analysis by developing algorithms that allow an efficient and reliable identification of rapid eye movements (REMs) and sleep EEG spindles. In the second part, developed methods were applied to sleep EEG data from a classical twin study to identify genetic effects on tonic and phasic REM sleep parameters, sleep spindles, and their trait-like characteristics. The algorithm for REM detection was developed for standard clinical two channel electrooculographic montage. The goal was to detect REMs visible above the background noise, and in the case of REM saccades to classify each movement separately. In order to achieve a high level of sensitivity, detection was based on a first derivative of electrooculogram (EOG) potentials and two detection thresholds. The developed REM detector was then validated in n=12 polysomnographic recordings from n=7 healthy subjects who had been previously scored visually by two human experts according to standard guidelines. Comparison of automatic REM detection with human scorers revealed mean correlations of 0.94 and 0.90, respectively (mean correlation between experts was 0.91). The developed automatic sleep spindle detector assessed individualized signal amplitude for each channel as well as slow and fast spindle frequency peaks based on the spectral analysis of the EEG signal. The spindle detection was based on Continuous Wavelet Transform (CWT); it localized the exact length of sleep spindles and was sensitive also for detection of sleep spindles intermingled in high amplitude slow wave EEG activity. The automatic spindle detector was validated in n=18 naps from n=10 subjects, where EEG data were scored both visually and by a commercial automatic algorithm (SIESTA). Comparison of our own spindle detector with results from the SIESTA algorithm and visual scoring revealed the correlations of 0.97 and 0.92, respectively (correlation between SIESTA algorithm and visual scoring was 0.90). In the second part of the work, the similarity of given sleep EEG parameters in n=32 healthy monozygotic (MZ) twins was compared with the similarity in n=14 healthy same-gender dizygotic (DZ) twins. The author of the current work did not participate in acquisition of twin study sample. EEG sleep recordings used for the heritability study were collected and already described by Ambrosius et al. (2008). Investigation of REM sleep included the absolute EEG spectral power, the shape of REM power spectrum, the amount and the structural organization of REMs; parameters of Non-REM sleep included slow and fast sleep spindle characteristics as well as the shape of the Non-REM power spectrum in general. In addition to estimating genetic effects, differences in within-pair similarity and night-to-night stability of given parameters were illustrated by intraclass correlation coefficients (ICC) and cluster analysis. A substantial genetic influence on both spectral composition and phasic parameters of REM sleep was observed. A significant genetic variance in spectral power affected delta to high sigma and high beta to gamma EEG frequency bands, as well as all phasic REM parameters with the exception of the REMs occurring outside REM bursts. Furthermore, MZ and DZ twins differed significantly in their within-pair similarity of non-REM and REM EEG spectra morphology. Regarding sleep spindles, statistical analysis revealed a significant genetic influence on localization in frequency range as well as on basic spindle characteristics (amplitude, length, quantity), except in the quantity of fast spindles in stage 2 and whole Non-REM sleep. Basic spindle parameters showed trait-like characteristics and significant differences in within-pair similarity between the twin groups. In summary, the developed algorithms for automatic REM and sleep spindle detection provide several advantages: the elimination of human scorer biases and intra-rater variability, investigation of structural organization of REMs, exact determination of fast and slow spindle frequency for each individual. Algorithms are fully automated and therefore well suited to score REM density and sleep spindles in large patient samples. In the second part of the study, sleep EEG analysis in MZ and DZ twins revealed a substantial genetic determination of both tonic and phasic REM sleep parameters. This complements previous findings of a high genetic determination of the Non-REM sleep power spectrum. Interestingly, smaller genetic effects and lower night-to-night stability were observed for fast spindles, especially in SWS. This is in line with recent hypotheses on the differential function of sleep spindle types for memory consolidation. The results from the presented studies strongly support the application of sleep EEG to identify clinically relevant biomarkers for psychiatric disorders

Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring

Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography signals, which is a tedious visual task requiring the workload of highly trained professionals. Consequently, research efforts to purse for an automatic stage scoring based on machine learning techniques have been carried out over the last years. In this work, we resort to multitaper spectral analysis to create visually interpretable images of sleep patterns from EEG signals as inputs to a deep convolutional network trained to solve visual recognition tasks. As a working example of transfer learning, a system able to accurately classify sleep stages in new unseen patients is presented. Evaluations in a widely-used publicly available dataset favourably compare to state-of-the-art results, while providing a framework for visual interpretation of outcomes.Comment: 8 pages, 1 figure, 2 tables, IEEE 2017 International Workshop on Machine Learning for Signal Processin

Airflow-Oximetry Combined Signal Based Automatic Detection of Sleep Apnea in Adults

University of Technology Sydney. Faculty of Engineering and Information Technology.Sleep apnea, a common sleep disorder, can significantly decrease the quality of life and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. It also elicits brain and physiological changes that vary across the night. Conventional diagnosis of sleep apnea using polysomnography (PSG) is costly and time-consuming, requiring manual scoring of sleep stages and respiratory events. Current automatic diagnostic algorithms used to detect sleep apnea vary in approaches with the use of different physiological signals. An effective, reliable, and accurate automatic method for the diagnosis of sleep apnea will be time-efficient and economical. This thesis is a narration of the work that led to the development of a novel algorithm suitable for the automatic diagnosis of sleep apnea. A systematic literature review of the existing methods (approaches and algorithms) was performed before designing the algorithm. This review presented an overview of methods to diagnose sleep apnea using respiratory and oximetry signals. The review identified the research gaps with indicating the major concerns, challenges, benefits, and limitations of using respiratory and oximetry signals for the diagnosis of sleep apnea. This thesis examined the electroencephalogram (EEG) spectral powers resulting from apnea duration of varying length and reported the changes in the relative powers in EEG frequency bands before and at apnea termination. The study was carried out for the purpose of justifying the usability of EEG for the automatic diagnosis of sleep apnea. It investigated the spectral power changes in delta, theta, alpha, sigma, and beta frequency bands of EEG as a function of apnea duration from 375 events. The study revealed a significant reduction in EEG relative powers (the low frequency theta, alpha, and sigma powers) both before and at apnea termination. The findings from the EEG spectral analysis suggests that the application of EEG signal in sleep apnea diagnosis is not reliable due to the random variations in spectral powers as well as the major challenges associated with EEG acquisition and its processing. Due to the limitations associated with the EEG for an unattended home diagnosis of sleep apnea, the EEG signal was excluded from the automatic detection approach […

Disturbed EEG Sleep, Paranoid Cognition and Somatic Symptoms Identify Veterans With Post-Traumatic Stress Disorder

Background Chronic post-traumatic stress disorder (PTSD) behavioural symptoms and medically unexplainable somatic symptoms are reported to occur following the stressful experience of military combatants in war zones. Aims To determine the contribution of disordered EEG sleep physiology in those military combatants who have unexplainable physical symptoms and PTSD behavioural difficulties following war-zone exposure. Method This case-controlled study compared 59 veterans with chronic sleep disturbance with 39 veterans with DSM-IV and clinician-administered PTSD Scale diagnosed PTSD who were unresponsive to pharmacological and psychological treatments. All had standardised EEG polysomnography, computerised sleep EEG cyclical alternating pattern (CAP) as a measure of sleep stability, self-ratings of combat exposure, paranoid cognition and hostility subscales of Symptom Checklist-90, Beck Depression Inventory and the Wahler Physical Symptom Inventory. Statistical group comparisons employed linear models, logistic regression and chi-square automatic interaction detection (CHAID)-like decision trees. Results Veterans with PTSD were more likely than those without PTSD to show disturbances in non-rapid eye movement (REM) and REM sleep including delayed sleep onset, less efficient EEG sleep, less stage 4 (deep) non-REM sleep, reduced REM and delayed onset to REM. There were no group differences in the prevalence of obstructive sleep apnoeas/hypopnoeas and periodic leg movements, but sleep-disturbed, non-PTSD military had more EEG CAP sleep instability. Rank order determinants for the diagnosis of PTSD comprise paranoid thinking, onset to REM sleep, combat history and somatic symptoms. Decision-tree analysis showed that a specific military event (combat), delayed onset to REM sleep, paranoid thinking and medically unexplainable somatic pain and fatigue characterise chronic PTSD. More PTSD veterans reported domestic and social misbehaviour. Conclusions Military combat, disturbed REM/non-REM EEG sleep, paranoid ideation and medically unexplained chronic musculoskeletal pain and fatigue are key factors in determining PTSD disability following war-zone exposure

EEG sleep stages identification based on weighted undirected complex networks

Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks. Methods each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks. Results In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals. Conclusions An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard