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

Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis

Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage from 53 participants with RBD and 53 age-matched healthy controls. Sleep stage classification was achieved using a Random Forest (RF) classifier and 156 features extracted from electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a RF classifier was trained combining established techniques to quantify muscle atonia with additional features that incorporate sleep architecture and the EMG fractal exponent. Automated multi-state sleep staging achieved a 0.62 Cohen's Kappa score. RBD detection accuracy improved by 10% to 96% (compared to individual established metrics) when using manually annotated sleep staging. Accuracy remained high (92%) when using automated sleep staging. This study outperforms established metrics and demonstrates that incorporating sleep architecture and sleep stage transitions can benefit RBD detection. This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation. This study validates a tractable, fully-automated, and sensitive pipeline for RBD identification that could be translated to wearable take-home technology.Comment: 20 pages, 3 figure

Robust, automated sleep scoring by a compact neural network with distributional shift correction.

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring

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

EXPERIMENTAL-COMPUTATIONAL ANALYSIS OF VIGILANCE DYNAMICS FOR APPLICATIONS IN SLEEP AND EPILEPSY

Epilepsy is a neurological disorder characterized by recurrent seizures. Sleep problems can cooccur with epilepsy, and adversely affect seizure diagnosis and treatment. In fact, the relationship between sleep and seizures in individuals with epilepsy is a complex one. Seizures disturb sleep and sleep deprivation aggravates seizures. Antiepileptic drugs may also impair sleep quality at the cost of controlling seizures. In general, particular vigilance states may inhibit or facilitate seizure generation, and changes in vigilance state can affect the predictability of seizures. A clear understanding of sleep-seizure interactions will therefore benefit epilepsy care providers and improve quality of life in patients. Notable progress in neuroscience research—and particularly sleep and epilepsy—has been achieved through experimentation on animals. Experimental models of epilepsy provide us with the opportunity to explore or even manipulate the sleep-seizure relationship in order to decipher different aspects of their interactions. Important in this process is the development of techniques for modeling and tracking sleep dynamics using electrophysiological measurements. In this dissertation experimental and computational approaches are proposed for modeling vigilance dynamics and their utility demonstrated in nonepileptic control mice. The general framework of hidden Markov models is used to automatically model and track sleep state and dynamics from electrophysiological as well as novel motion measurements. In addition, a closed-loop sensory stimulation technique is proposed that, in conjunction with this model, provides the means to concurrently track and modulate 3 vigilance dynamics in animals. The feasibility of the proposed techniques for modeling and altering sleep are demonstrated for experimental applications related to epilepsy. Finally, preliminary data from a mouse model of temporal lobe epilepsy are employed to suggest applications of these techniques and directions for future research. The methodologies developed here have clear implications the design of intelligent neuromodulation strategies for clinical epilepsy therapy

Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep

n/

Automated sleep classification using the new sleep stage standards

Sleep is fundamental for physical health and good quality of life, and clinicians and researchers have long debated how best to understand it. Manual approaches to sleep classification have been in use for over 40 years, and in 2007, the American Academy of Sleep Medicine (AASM) published a new sleep scoring manual. Over the years, many attempts have been made to introduce and validate machine learning and automated classification techniques in the sleep research field, with the goals of improving consistency and reliability. This thesis explored and assessed the use of automated classification systems with the updated sleep stage definitions and scoring rules using neuro-fuzzy system (NFS) and support vector machine (SVM) methodology. For both the NFS and SVM classification techniques, the overall percent correct was approximately 65%, with sensitivity and specificity rates around 80% and 95%, respectively. The overall Kappa scores, one means for evaluating system reliability, were approximately 0.57 for both the NFS and SVM, indicating moderate agreement that is not accidental. Stage 3 sleep was detected with an 87-89% success rate. The results presented in this thesis show that the use of NFS and SVM methods for classifying sleep stages is possible using the new AASM guidelines. While the current work supports and confirms the use of these classification techniques within the research community, the results did not indicate a significant difference in the accuracy of either approach-nor a difference in one over the other. The results suggest that the important clinical stage 3 (slow wave sleep) can be accurately scored with these classifiers; however, the techniques used here would need more investigation and optimization prior to serious use in clinical applications

Low-complexity algorithms for automatic detection of sleep stages and events for use in wearable EEG systems

Objective: Diagnosis of sleep disorders is an expensive procedure that requires performing a sleep study, known as polysomnography (PSG), in a controlled environment. This study monitors the neural, eye and muscle activity of a patient using electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) signals which are then scored in to different sleep stages. Home PSG is often cited as an alternative of clinical PSG to make it more accessible, however it still requires patients to use a cumbersome system with multiple recording channels that need to be precisely placed. This thesis proposes a wearable sleep staging system using a single channel of EEG. For realisation of such a system, this thesis presents novel features for REM sleep detection from EEG (normally detected using EMG/EOG), a low-complexity automatic sleep staging algorithm using a single EEG channel and its complete integrated circuit implementation. Methods: The difference between Spectral Edge Frequencies (SEF) at 95% and 50% in the 8-16 Hz frequency band is shown to have high discriminatory ability for detecting REM sleep stages. This feature, together with other spectral features from single-channel EEG are used with a set of decision trees controlled by a state machine for classification. The hardware for the complete algorithm is designed using low-power techniques and implemented on chip using 0.18μm process node technology. Results: The use of SEF features from one channel of EEG resulted in 83% of REM sleep epochs being correctly detected. The automatic sleep staging algorithm, based on contextually aware decision trees, resulted in an accuracy of up to 79% on a large dataset. Its hardware implementation, which is also the very first complete circuit level implementation of any sleep staging algorithm, resulted in an accuracy of 98.7% with great potential for use in fully wearable sleep systems.Open Acces