Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal

A microsleep is an unintentional, transient loss of consciousness correlated with sleep that lasts up to fifteen seconds. Electroencephalogram (EEG), recordings have been extensively utilized to diagnose and study various neurological disorders. This study analyzes time series EEG signals to predict microsleep employing two deep learning models: Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN). The findings show that the ANN model achieves outstanding metrics in microsleep prediction, outperforming the LSTM in key performance metrics. The model demonstrated exceptional performance, as demonstrated by the outcomes of the Scatter Plot, R2 Score, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Between the two models, the ANN model achieved the most significant R2, MAE, MSE, and RMSE values (0.84, 1.10, 1.90, and 1.38) compared to the LSTM model. The critical contribution of this study lies in its development of comprehensive and effective methods for accurately predicting microsleep events from EEG signals

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Alertness fluctuations when performing a task modulate cortical evoked responses to transcranial magnetic stimulation.

Transcranial magnetic stimulation (TMS) has been widely used in human cognitive neuroscience to examine the causal role of distinct cortical areas in perceptual, cognitive and motor functions. However, it is widely acknowledged that the effects of focal cortical stimulation can vary substantially between participants and even from trial to trial within individuals. Recent work from resting state functional magnetic resonance imaging (fMRI) studies has suggested that spontaneous fluctuations in alertness over a testing session can modulate the neural dynamics of cortical processing, even when participants remain awake and responsive to the task at hand. Here we investigated the extent to which spontaneous fluctuations in alertness during wake-to-sleep transition can account for the variability in neurophysiological responses to TMS. We combined single-pulse TMS with neural recording via electroencephalography (EEG) to quantify changes in motor and cortical reactivity with fluctuating levels of alertness defined objectively on the basis of ongoing brain activity. We observed rapid, non-linear changes in TMS-evoked responses with decreasing levels of alertness, even while participants remained responsive in the behavioural task. Specifically, we found that the amplitude of motor evoked potentials peaked during periods of EEG flattening, whereas TMS-evoked potentials increased and remained stable during EEG flattening and the subsequent occurrence of theta ripples that indicate the onset of NREM stage 1 sleep. Our findings suggest a rapid and complex reorganization of active neural networks in response to spontaneous fluctuations of alertness over relatively short periods of behavioural testing during wake-to-sleep transition

Correcting inter-sectional accuracy differences in drowsiness detection systems using generative adversarial networks (GANs)

Doctoral Degrees. University of KwaZulu-Natal, Durban.oad accidents contribute to many injuries and deaths among the human population. There is substantial evidence that proves drowsiness is one of the most prominent causes of road accidents all over the world. This results in fatalities and severe injuries for drivers, passengers, and pedestrians. These alarming facts are raising the interest in equipping vehicles with robust driver drowsiness detection systems to minimise accident rates. One of the primary concerns of motor industries is the safety of passengers and as a consequence they have invested significantly in research and development to equip vehicles with systems that can help minimise to road accidents. A number research endeavours have attempted to use Artificial intelligence, and particularly Deep Neural Networks (DNN), to build intelligent systems that can detect drowsiness automatically. However, datasets are crucial when training a DNN. When datasets are unrepresentative, trained models are prone to bias because they are unable to generalise. This is particularly problematic for models trained in specific cultural contexts, which may not represent a wide range of races, and thus fail to generalise. This is a specific challenge for driver drowsiness detection task, where most publicly available datasets are unrepresentative as they cover only certain ethnicity groups. This thesis investigates the problem of an unrepresentative dataset in the training phase of Convolutional Neural Networks (CNNs) models. Firstly, CNNs are compared with several machine learning techniques to establish their superior suitability for the driver drowsiness detection task. An investigation into the implementation of CNNs was performed and highlighted that publicly available datasets such as NTHU, DROZY and CEW do not represent a wide spectrum of ethnicity groups and lead to biased systems. A population bias visualisation technique was proposed to help identify the regions, or individuals where a model is failing to generalise on a picture grid. Furthermore, the use of Generative Adversarial Networks (GANs) with lightweight convolutions called Depthwise Separable Convolutions (DSC) for image translation to multi-domain outputs was investigated in an attempt to generate synthetic datasets. This thesis further showed that GANs can be used to generate more realistic images with varied facial attributes for predicting drowsiness across multiple ethnicity groups. Lastly, a novel framework was developed to detect bias and correct it using synthetic generated images which are produced by GANs. Training models using this framework results in a substantial performance boost

CES-513 Stages for Developing Control Systems using EMG and EEG Signals: A survey

Bio-signals such as EMG (Electromyography), EEG (Electroencephalography), EOG (Electrooculogram), ECG (Electrocardiogram) have been deployed recently to develop control systems for improving the quality of life of disabled and elderly people. This technical report aims to review the current deployment of these state of the art control systems and explain some challenge issues. In particular, the stages for developing EMG and EEG based control systems are categorized, namely data acquisition, data segmentation, feature extraction, classification, and controller. Some related Bio-control applications are outlined. Finally a brief conclusion is summarized.

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

Slow-wave sleep : generation and propagation of slow waves, role in long-term plasticity and gating

Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2012-2013.Le sommeil est connu pour réguler plusieurs fonctions importantes pour le cerveau et parmi celles-ci, il y a le blocage de l’information sensorielle par le thalamus et l’amélioration de la consolidation de la mémoire. Le sommeil à ondes lentes, en particulier, est considéré être critique pour ces deux processus. Cependant, leurs mécanismes physiologiques sont inconnus. Aussi, la marque électrophysiologique distinctive du sommeil à ondes lentes est la présence d’ondes lentes de grande amplitude dans le potentiel de champ cortical et l’alternance entre des périodes d’activités synaptiques intenses pendant lesquelles les neurones corticaux sont dépolarisés et déchargent plusieurs potentiels d’action et des périodes silencieuses pendant lesquelles aucune décharge ne survient, les neurones corticaux sont hyperpolarisés et très peu d’activités synaptiques sont observées. Tout d'abord, afin de mieux comprendre les études présentées dans ce manuscrit, une introduction générale couvrant l'architecture du système thalamocortical et ses fonctions est présentée. Celle-ci comprend une description des états de vigilance, suivie d'une description des rythmes présents dans le système thalamocortical au cours du sommeil à ondes lentes, puis par une description des différents mécanismes de plasticité synaptique, et enfin, deux hypothèses sur la façon dont le sommeil peut affecter la consolidation de la mémoire sont présentées. Puis, trois études sont présentées et ont été conçues pour caractériser les propriétés de l'oscillation lente du sommeil à ondes lentes. Dans la première étude (chapitre II), nous avons montré que les périodes d'activité (et de silence) se produisent de façon presque synchrone dans des neurones qui ont jusqu'à 12 mm de distance. Nous avons montré que l'activité était initiée en un point focal et se propageait rapidement à des sites corticaux voisins. Étonnamment, le déclenchement des états silencieux était encore plus synchronisé que le déclenchement des états actifs. L'hypothèse de travail pour la deuxième étude (chapitre III) était que les états actifs sont générés par une sommation de relâches spontanées de médiateurs. Utilisant différents enregistrements à la fois chez des animaux anesthésiés et chez d’autres non-anesthésiés, nous avons montré qu’aucune décharge neuronale ne se produit dans le néocortex pendant les états silencieux du sommeil à ondes lentes, mais certaines activités synaptiques peuvent ii être observées avant le début des états actifs, ce qui était en accord avec notre hypothèse. Nous avons également montré que les neurones de la couche V étaient les premiers à entrer dans l’état actif pour la majorité des cycles, mais ce serait ainsi uniquement pour des raisons probabilistes; ces cellules étant équipées du plus grand nombre de contacts synaptiques parmi les neurones corticaux. Nous avons également montré que le sommeil à ondes lentes et l’anesthésie à la kétamine-xylazine présentent de nombreuses similitudes. Ayant utilisé une combinaison d'enregistrements chez des animaux anesthésiés à la kétamine-xylazine et chez des animaux non-anesthésiés, et parce que l'anesthésie à la kétamine-xylazine est largement utilisée comme un modèle de sommeil à ondes lentes, nous avons effectué des mesures quantitatives des différences entre les deux groupes d'enregistrements (chapitre IV). Nous avons trouvé que l'oscillation lente était beaucoup plus rythmique sous anesthésie et elle était aussi plus cohérente entre des sites d’enregistrements distants en comparaison aux enregistrements de sommeil naturel. Sous anesthésie, les ondes lentes avaient également une amplitude plus grande et une durée plus longue par rapport au sommeil à ondes lentes. Toutefois, les ondes fuseaux (spindles) et gamma étaient également affectées par l'anesthésie. Dans l'étude suivante (Chapitre V), nous avons investigué le rôle du sommeil à ondes lentes dans la formation de la plasticité à long terme dans le système thalamocortical. À l’aide de stimulations pré-thalamiques de la voie somatosensorielle ascendante (fibres du lemnisque médial) chez des animaux non-anesthésiés, nous avons montré que le potentiel évoqué enregistré dans le cortex somatosensoriel était augmenté dans une période d’éveil suivant un épisode de sommeil à ondes lentes par rapport à l’épisode d’éveil précédent et cette augmentation était de longue durée. Nous avons également montré que le sommeil paradoxal ne jouait pas un rôle important dans cette augmentation d'amplitude des réponses évoquées. À l’aide d'enregistrements in vitro en mode cellule-entière, nous avons caractérisé le mécanisme derrière cette augmentation et ce mécanisme est compatible avec la forme classique de potentiation à long terme, car il nécessitait une activation à la fois les récepteurs NMDA et des récepteurs AMPA, ainsi que la présence de calcium dans le neurone post-synaptique. iii La dernière étude incluse dans cette thèse (chapitre VI) a été conçue pour caractériser un possible mécanisme physiologique de blocage sensoriel thalamique survenant pendant le sommeil. Les ondes fuseaux sont caractérisées par la présence de potentiels d’action calcique à seuil bas et le calcium joue un rôle essentiel dans la transmission synaptique. En utilisant plusieurs techniques expérimentales, nous avons vérifié l'hypothèse que ces potentiels d’action calciques pourraient causer un appauvrissement local de calcium dans l'espace extracellulaire ce qui affecterait la transmission synaptique. Nous avons montré que les canaux calciques responsables des potentiels d’action calciques étaient localisés aux synapses et que, de fait, une diminution locale de la concentration extracellulaire de calcium se produit au cours d’un potentiel d’action calcique à seuil bas spontané ou provoqué, ce qui était suffisant pour nuire à la transmission synaptique. Nous concluons que l'oscillation lente est initiée en un point focal et se propage ensuite aux aires corticales voisines de façon presque synchrone, même pour des cellules séparées par jusqu'à 12 mm de distance. Les états actifs de cette oscillation proviennent d’une sommation de relâches spontanées de neuromédiateurs (indépendantes des potentiels d’action) et cette sommation peut survenir dans tous neurones corticaux. Cependant, l’état actif est généré plus souvent dans les neurones pyramidaux de couche V simplement pour des raisons probabilistes. Les deux types d’expériences (kétamine-xylazine et sommeil à ondes lentes) ont montré plusieurs propriétés similaires, mais aussi quelques différences quantitatives. Nous concluons également que l'oscillation lente joue un rôle essentiel dans l'induction de plasticité à long terme qui contribue très probablement à la consolidation de la mémoire. Les ondes fuseaux, un autre type d’ondes présentes pendant le sommeil à ondes lentes, contribuent au blocage thalamique de l'information sensorielle.Sleep is known to mediate several major functions in the brain and among them are the gating of sensory information during sleep and the sleep-related improvement in memory consolidation. Slow-wave sleep in particular is thought to be critical for both of these processes. However, their physiological mechanisms are unknown. Also, the electrophysiological hallmark of slow-wave sleep is the presence of large amplitude slow waves in the cortical local field potential and the alternation of periods of intense synaptic activity in which cortical neurons are depolarized and fire action potentials and periods of silence in which no firing occurs, cortical neurons are hyperpolarized, and very little synaptic activities are observed. First, in order to better understand the studies presented in this manuscript, a general introduction covering the thalamocortical system architecture and function is presented, which includes a description of the states of vigilance, followed by a description of the rhythms present in the thalamocortical system during slow-wave sleep, then by a description of the mechanisms of synaptic plasticity, and finally two hypotheses about how sleep might affect the consolidation of memory are presented. Then, three studies are presented and were designed to characterize the properties of the sleep slow oscillation. In the first study (Chapter II), we showed that periods of activity (and silence) occur almost synchronously in neurons that are separated by up to 12 mm. The activity was initiated in a focal point and rapidly propagated to neighboring sites. Surprisingly, the onsets of silent states were even more synchronous than onsets of active states. The working hypothesis for the second study (Chapter III) was that active states are generated by a summation of spontaneous mediator releases. Using different recordings in both anesthetized and non-anesthetized animals, we showed that no neuronal firing occurs in the neocortex during silent states of slow-wave sleep but some synaptic activities might be observed prior to the onset of active states, which was in agreement with our hypothesis. We also showed that layer V neurons were leading the onset of active states in most of the cycles but this would be due to probabilistic reasons; these cells being equipped with the most numerous synaptic contacts among cortical neurons. We also showed that slow-wave sleep and ketamine-xylazine shares many similarities. v Having used a combination of recordings in ketamine-xylazine anesthetized and non-anesthetized animals, and because ketamine-xylazine anesthesia is extensively used as a model of slow-wave sleep, we made quantitative measurements of the differences between the two groups of recordings (Chapter IV). We found that the slow oscillation was much more rhythmic under anesthesia and it was also more coherent between distant sites as compared to recordings during slow-wave sleep. Under anesthesia, slow waves were also of larger amplitude and had a longer duration as compared to slow-wave sleep. However, spindles and gamma were also affected by the anesthesia. In the following study (Chapter V), we investigated the role of slow-wave sleep in the formation of long-term plasticity in the thalamocortical system. Using pre-thalamic stimulations of the ascending somatosensory pathway (medial lemniscus fibers) in non-anesthetized animals, we showed that evoked potential recorded in the somatosensory cortex were enhanced in a wake period following a slow-wave sleep episode as compared to the previous wake episode and this enhancement was long-lasting. We also showed that rapid eye movement sleep did not play a significant role in this enhancement of response amplitude. Using whole-cell recordings in vitro, we characterized the mechanism behind this enhancement and it was compatible with the classical form of long-term potentiation, because it required an activation of both NMDA and AMPA receptors as well as the presence of calcium in the postsynaptic neuron. The last study included in this thesis (Chapter VI) was designed to characterise a possible physiological mechanism of thalamic sensory gating occurring during sleep. Spindles are characterized by the presence of low-threshold calcium spikes and calcium plays a critical role in the synaptic transmission. Using several experimental techniques, we verified the hypothesis that these calcium spikes would cause a local depletion of calcium in the extracellular space which would impair synaptic transmission. We showed that calcium channels responsible for calcium spikes were co-localized with synapses and that indeed, local extracellular calcium depletion occurred during spontaneous or induced low-threshold calcium spike, which was sufficient to impair synaptic transmission. We conclude that slow oscillation originate at a focal point and then propagate to neighboring cortical areas being almost synchronous even in cells located up to 12 mm vi apart. Active states of this oscillation originate from a summation of spike-independent mediator releases that might occur in any cortical neurons, but happens more often in layer V pyramidal neurons simply due to probabilistic reasons. Both experiments in ketamine-xylazine anesthesia and non-anesthetized animals showed several similar properties, but also some quantitative differences. We also conclude that slow oscillation plays a critical role in the induction of long-term plasticity, which very likely contributes to memory consolidation. Spindles, another oscillation present in slow-wave sleep, contribute to the thalamic gating of information

Predicting Sleepiness from Driving Behaviour

This research investigates the use of objective EEG analysis to determine multiple levels of sleepiness in drivers. In the literature, current methods propose a binary (awake or sleep) or ternary (awake, drowsy or sleep) classification of sleepiness. Having few classification of sleepiness increases the risk of the driver reaching dangerous levels of sleepiness before a safety system can prevent it. Also, these methods are based on subjective analysis of physiological variables, which leads to lack of reproducibility and loss of data, when a lack of consensus is reached amongst the EEG experts. Therefore, the doctoral challenge was to determine whether multiple levels of sleepiness could be defined with high accuracy, using an objective analysis of EEG, a reliable indicator of sleepiness. The study identified awake, post-awake, pre-sleep and sleep as the multiple levels of sleepiness through the objective analysis of EEG. The research used Neural Networks, a type of Machine Learning algorithm, to determine the accuracy of the proposed multiple levels of sleepiness. The Neural Networks were trained using driving and physiological behaviour. The EEG data and the driving and physiological variables were obtained through a series of experiments aimed to induce sleepiness, conducted in the driving simulator at the University of Leeds. As the Neural Network obtained high accuracy when differentiating between awake and sleep and between post-awake and pre-sleep, it led to the conclusion that the proposed objective classification based on objective EEG analysis was suitable. However, this study did not reach the highest levels of accuracy when the 4 levels of sleepiness are combined, nevertheless the solutions proposed by the researcher to be carried in future work can contribute towards increasing the accuracy of the proposed method

Analysis of Signal Decomposition and Stain Separation methods for biomedical applications

Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis