Classification of awake, REM, and NREM from EEG via singular spectrum analysis

© 2015 IEEE. In this study, a single-channel electroencephalography (EEG) analysis method has been proposed for automated 3-state-sleep classification to discriminate Awake, NREM (non-rapid eye movement) and REM (rapid eye movement). For this purpose, singular spectrum analysis (SSA) is applied to automatically extract four brain rhythms: delta, theta, alpha, and beta. These subbands are then used to generate the appropriate features for sleep classification using a multi class support vector machine (M-SVM). The proposed method provided 0.79 agreement between the manual and automatic scores

Improving time–frequency domain sleep EEG classification via singular spectrum analysis

Background: Manual sleep scoring is deemed to be tedious and time consuming. Even among automatic methods such as Time-Frequency (T-F) representations, there is still room for more improvement. New method: To optimise the efficiency of T-F domain analysis of sleep electroencephalography (EEG) a novel approach for automatically identifying the brain waves, sleep spindles, and K-complexes from the sleep EEG signals is proposed. The proposed method is based on singular spectrum analysis (SSA). The single-channel EEG signal (C3-A2) is initially decomposed and then the desired components are automatically separated. In addition, the noise is removed to enhance the discrimination ability of features. The obtained T-F features after preprocessing stage are classified using a multi-class support vector machines (SVM) and used for the identification of four sleep stages over three sleep types. Furthermore, to emphasize on the usefulness of the proposed method the automatically-determined spindles are parameterised to discriminate three sleep types. Result: The four sleep stages are classified through SVM twice: with and without preprocessing stage. The mean accuracy, sensitivity, and specificity for before the preprocessing stage are: 71.5 ± 0.11%, 56.1 ± 0.09% and 86.8 ± 0.04% respectively. However, these values increase significantly to 83.6 ± 0.07%, 70.6 ± 0.14% and 90.8 ± 0.03% after applying SSA. Comparison with existing method: The new T-F representation has been compared with the existing benchmarks. Our results prove that, the proposed method well outperforms the previous methods in terms of identification and representation of sleep stages. Conclusion: Experimental results confirm the performance improvement in terms of classification rate and also representative T-F domain

Quaternion singular spectrum analysis of electroencephalogram With application in sleep analysis

A novel quaternion-valued singular spectrum analysis (SSA) is introduced for multichannel analysis of electroencephalogram (EEG). The analysis of EEG typically requires the decomposition of data channels into meaningful components despite the notoriously noisy nature of EEG - which is the aim of SSA. However, the singular value decomposition involved in SSA implies the strict orthogonality of the decomposed components, which may not reflect accurately the sources which exhibit similar neural activities. To allow for the modelling of such co-channel coupling, the quaternion domain is considered for the first time to formulate the SSA using the augmented statistics. As an application, we demonstrate how the augmented quaternion-valued SSA (AQSSA) can be used to extract the sources, even at a signal-to-noise ratio as low as -10 dB. To illustrate the usefulness of our quaternion-valued SSA in a rehabilitation setting, we employ the proposed SSA for sleep analysis to extract statistical descriptors for five-stage classification (Awake, N1, N2, N3 and REM). The level of agreement using these descriptors was 74% as quantified by the Cohen's kappa

Investigation and Modelling of Fetal Sheep Maturation

In this thesis, I study the maturational changes of the fetal sheep ECoG (electrocorticogram) in its third-trimester of gestation (95-140 days of gestation), investigate three continuum models for electrical behaviour of the cortex, and tune the parameters in one of these models to generate the discontinuous EEG waves in the immature cortex. Visual inspection of the ECoG time-series shows that the third-trimester of fetal sheep is comprised of two stages: early third-trimester characterised by bursting activity separated by silent intervals, and late third-trimester with well-defined SWS (slow wave sleep) and REM (rapid eye movement) sleep states. For the late third-trimester, the results of power, correlation time, and SVD (singular value decomposition) entropy analysis demonstrate that the sleep state change is a cortical phase transition—with SWS-to-REM transition being a first-order transition, and REM-to-SWS second-order. Further analyses by correlation time, SVD entropy, and spectral edge frequency display that the differentiation of the two distinct SWS and REM sleep states occurs at about 125 dGA (day gestational age). Spectral analysis divides the third-trimester into four stages in terms of the frequency and amplitude variations of the major resonances. Spindle-like resonances only occur in the first stage. A power surge is observed immediately prior to the emergence of the two sleep states. Most significant changes of the spectrum occur during the fourth stage for both SWS (in amplitude) and REM (in frequency) sleep states. For the modelling of the immature cortex, different theoretical descriptions of cortical behaviour are investigated, including the ccf (cortical column field) model of J. J. Wright, and the Waikato cortical model. For the ccf model at centimetric scale, the time-series, fluctuation power, power law relation, gamma oscillation, phase relation between excitatory and inhibitory elements, power spectral density, and spatial Fourier spectrum are quantified from numerical simulations. From these simulations, I determined that the physiologically sophisticated ccf model is too large and unwieldy for easy tuning to match the electrical response of the immature cortex. The Waikato near-far fast-soma model is constructed by incorporating the back-propagation effect of the action potential into the Waikato fast-soma model, state equations are listed and stability prediction are performed by varying the gap junction diffusion strength, subcortical drive, and the rate constants of the near- and far-dendritic tree. In the end, I selected the classic and simpler Waikato slow-soma mean-field model to use for my immature cortex simulations. Model parameters are customised based on the physiology of the immature cortex, including GABA (an inhibitory neurotransmitter in adult) excitatory effect, number of synaptic connections, and rate constants of the IPSPs (inhibitory postsynaptic potential). After hyperpolarising the neuron resting voltage sufficiently to cause the immature inhibitory neuron to act as an excitatory agent, I alter the rate constant of the IPSP, and study the stability of the immature cortex. The bursting activity and quiet states of the discontinuous EEG are simulated and the gap junction diffusion effect in the immature cortex is also examined. For a rate constant of 18.6 s-1, slow oscillations in the quiet states are generated, and for rate constant of 25 s-1, a possible cortical network oscillation emerges. As far as I know, this is the first time that the GABA excitatory effect has been integrated into a mean-field cortical model and the discontinuous EEG wave successfully simulated in a qualitative way

Sleep Stage Classification: A Deep Learning Approach

Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity

The effect of cognitive training on subsequent sleep characteristics

Introduction: Several studies have consistently shown that pre-sleep learning produces changes in sleep structure. Whereas the majority of these studies has mainly focused on post-training changes in sleep states (namely REM and NREM sleep amount) and, more recently, in specific electrophysiological features (e.g., sleep spindles, slow wave activity), very little attention has been paid to the hypothesis that pre-sleep learning might improve sleep quality, as expressed by sleep continuity, stability and cyclic organization measures. Furthermore, studies addressing the relationship between sleep and learning usually employ purely declarative or procedural tasks, neglecting that everyday life learning processes depend on the simultaneous activation of different memory systems. Recently, we have reported that a complex ecological learning task (requiring the simultaneous activation of several cognitive functions), intensively administered at bedtime, improves daytime sleep continuity and stability, possibly as a result of ongoing memory processes. To follow up our previous study, here we aimed to extend these findings to a night paradigm and to test whether a similar post-training sleep improvement may be obtained in a sample of individuals with sleep complaints. Specifically, our focus was on post-training changes in objective and subjective sleep quality. Furthermore, we compared overnight performance changes with those obtained over a wake retention period, in order to address the possible differential effect of sleep and wake on memory processes. Method: After a habituation night, twenty-one subjects (F=15, mean age: 27.5±7.7 years, all bad sleepers according to the Pittsburgh Sleep Quality Index) underwent conventional polygraphic recording under three conditions: 1) BL, baseline night sleep; 2) post-active control sleep (AC), a sleep episode preceded by a non-learning control task; 3) post-training sleep (TR), a sleep episode preceded by a complex ecological task. The same task as in TR was administered in a Wake condition (W), in which the retention period between training sessions corresponded to the duration of the subject’s baseline sleep time. Subjects underwent AC, TR and W conditions in balanced order. The complex cognitive task consisted in a slightly modified version of the famous word game “Ruzzle”. In this game, the player has two minutes to form as many words as possible and reach the highest score achievable with the 16 letters available in a 4x4 grid on an iPad screen. Performance measures were R-WORDS%, i.e., the number of detected words over total available words, and R-SCORE%, i.e., the global score achieved, depending on the number of words found, on their length and on the ability to use the coloured bonus letters which multiply letter or word values. Results: Post-training sleep (TR) showed a reduction in Stage 1 proportion (F=4.39, p=.021; TRTR and AC) and brief awakenings frequency (F=5.89, p=.007, BL>TR and AC), decreased frequency of arousals (F=6.25, p=.005; TRTR and AC) and functional uncertainty (FU) periods (F=14.23, pTR and AC), as well as a reduction of time spent in FU periods (F=515.33, pTR and AC); an increase in the number of NREM-REM cycles (F=4.51, p=.019; TR>BL and AC), and of time spent in cycles (F=4.77, p=.015; TR>BL and AC). This improvement in objective sleep quality was paralleled by that in subjective ratings, assessed through the Self-Rating Scale for Sleep and Awakenings Quality (χ2=9.13, p=.010; TRW), while the opposite effect emerged for the R-WORDS% (t=-2.96, p=.01; W>TR). Conclusions: Our results extend previous findings on post-training changes in sleep continuity, stability and organization to a sample of bad sleepers; also, they show that objective sleep improvement may be reflected in subjective sleep quality perception. Interestingly, the active control task also produced improvements in some of these features, prompting future investigations on the contribution to post-training sleep changes of additional factors not specifically linked to learning processes. As for performance, the finding of a significant sleep effect for the more complex performance measure (R-SCORE%) suggests that sleep preferentially promotes effective learning of elaborate cognitive strategies rather than that of simpler cognitive processes. In conclusion, in light of the importance of non-pharmacological treatments for sleep disturbances, this study offers the possibility to further explore planned cognitive training as a low-cost treatment strategy to improve sleep quality

An investigation into the role of MeCP2 in sleep-related brain rhythms and memory consolidation

Methyl-CpG binding protein 2 (MeCP2) is a chromatin-associated protein which functions in epigenetic gene regulation. Mutations in MeCP2 lead to a variety of neurological disorders, including Rett Syndrome (RTT). Learning and memory deficits are prevalent in RTT, as are sleep disturbances: throughout the night, RTT patients spend less time in Stage 3 non-rapid eye movement (NREM) sleep. Delta oscillations (0.5 – 4 Hz) are the main constituent of Stage 3 NREM sleep, and are thought to be vital for sleep-related memory consolidation. In this thesis, the mechanisms and networks involved in delta oscillation generation were studied in a mouse model of RTT. In isolated sections of somatosensory cortex, loss of MeCP2 function resulted in the disruption of pharmacologically-induced cortical delta oscillations. In contrast, delta oscillations that arise via the thalamic generator remained intact. Pre-symptomatic Mecp2-null animals showed partial preservation of cortical delta oscillations, suggesting that neurological deficits precede phenotype onset. Intracellular current clamp recordings revealed that loss of MeCP2 function impairs the firing pattern of layer V intrinsically bursting pyramidal neurons, the cells responsible for generating the cortical delta rhythm. The bursting mechanism of these cells was restored by reducing the intracellular calcium ion concentration in these cells, which was also sufficient to reinstate the cortical delta rhythm. Finally, delta oscillations, sleep spindles and hippocampal sharp-wave ripples were studied in vivo during NREM sleep, since the coupling of these rhythms is thought to facilitate sleep-related memory consolidation. Rhythm coupling was unaffected by the loss of MeCP2 function, however the incidence of all three rhythms was significantly reduced, resulting in impaired performance on a hippocampus-dependent spatial memory task. The implication of these results on our understanding of the precise role of MeCP2 in coordinating NREM-associated brain rhythms and on the development of learning and memory deficits in RTT are discussed