A knowledge model for the development of a framework for hypnogram construction

The final publication is available via http://dx.doi.org/10.1016/j.knosys.2016.11.016[Abstract] We describe a proposal of a knowledge model for the development of a framework for hypnogram construction from intelligent analysis of pulmonology and electrophysiological signals. Throughout the twentieth century, after the development of electroencephalography (EEG) by Hans Berger, there have been multiple studies on human sleep and its structure. Polysomnography (PSG), a sleep study from several biophysiological variables, gives us the hypnogram, a graphic representation of the stages of sleep as a function of time. This graph, when analyzed in conjunction with other physiological parameters, such as the heart rate or the amount of oxygen in arterial blood, has become a valuable diagnostic tool for different clinical problems that can occur during sleep and that often cause poor quality sleep. Currently, the gold standard for the detection of sleep events and for the correct classification of sleep stages are the rules published by the American Academy of Sleep Medicine (AASM), version 2.2. Based on the standards available to date, different studies on methods of automatic analysis of sleep and its stages have been developed but because of the different development and validation procedures used in existing methods, a rigorous and useful comparative analysis of results and their ability to correctly classify sleep stages is not possible. In this sense, we propose an approach that ensures that sleep stage classification task is not affected by the method for extracting PSG features and events. This approach is based on the development of a knowledge-intensive base system (KBS) for classifying sleep stages and building the corresponding hypnogram. For this development we used the CommonKADS methodology, that has become a de facto standard for the development of KBSs. As a result, we present a new knowledge model that can be used for the subsequent development of an intelligent system for hypnogram construction that allows us to isolate the process of signal processing to identify sleep stages so that the hypnograms obtained become comparable, independently of the signal analysis techniques.Xunta de Galicia; GRC2014/035Ministerio de Economía y Competitividad; TIN2013-40686-

A Convolutional Network for Sleep Stages Classification

[Abstract]: Sleep stages classification is a crucial task in the context of sleep studies. It involves the simultaneous analysis of multiple signals recorded during sleep. However, it is complex and tedious, and even the trained expert can spend several hours scoring a single night recording. Multiple automatic methods have tried to solve these problems in the past, most of them by classifying a feature vector that is engineered for a specific dataset. In this work, we avoid this bias using a deep learning model that learns relevant features without human intervention. Particularly, we propose an ensemble of 5 convolutional networks that achieves a kappa index of 0.83 when classifying a dataset of 500 sleep recordings

Diagnosis of the sleep apnea-hypopnea syndrome : a comprehensive approach through an intelligent system to support medical decision

[Abstract] This doctoral thesis carries out the development of an intelligent system to support medical decision in the diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS). SAHS is the most common disorder within those affecting sleep. The estimates of the disease prevalence range from 3% to 7%. Diagnosis of SAHS requires of a polysomnographic test (PSG) to be done in the Sleep Unit of a medical center. Manual scoring of the resulting recording entails too much effort and time to the medical specialists and as a consequence it implies a high economic cost. In the developed system, automatic analysis of the PSG is accomplished which follows a comprehensive perspective. Firstly an analysis of the neurophysiological signals related to the sleep function is carried out in order to obtain the hypnogram. Then, an analysis is performed over the respiratory signals which have to be subsequently interpreted in the context of the remaining signals included in the PSG. In order to carry out such a task, the developed system is supported by the use of artificial intelligence techniques, specially focusing on the use of reasoning mechanisms capable of handling data imprecision. Ultimately, it is the aim of the proposed system to improve the diagnostic procedure and help physicians in the diagnosis of SAHS.[Resumen] Esta tesis aborda el desarrollo de un sistema inteligente de apoyo a la decisión clínica para el diagnóstico del Síndrome de Apneas-Hipopneas del Sueño (SAHS). El SAHS es el trastorno más común de aquellos que afectan al sueño. Afecta a un rango del 3% al 7% de la población con consecuencias severas sobre la salud. El diagnóstico requiere la realización de un análisis polisomnográfico (PSG) en una Unidad del Sueño de un centro hospitalario. El análisis manual de dicha prueba resulta muy costoso en tiempo y esfuerzo para el médico especialista, y como consecuencia en un elevado coste económico. El sistema desarrollado lleva a cabo el análisis automático del PSG desde una perspectiva integral. A tal efecto, primero se realiza un análisis de las señales neurofisiológicas vinculadas al sueño para obtener el hipnograma, y seguidamente, se lleva a cabo un análisis neumológico de las señales respiratorias interpretándolas en el contexto que marcan las demás señales del PSG. Para lleva a cabo dicha tarea el sistema se apoya en el uso de distintas técnicas de inteligencia artificial, con especial atención al uso mecanismos de razonamiento con soporte a la imprecisión. El principal objetivo del sistema propuesto es la mejora del procedimiento diagnóstico y ayudar a los médicos en diagnóstico del SAHS.[Resumo] Esta tese aborda o desenvolvemento dun sistema intelixente de apoio á decisión clínica para o diagnóstico do Síndrome de Apneas-Hipopneas do Sono (SAHS). O SAHS é o trastorno máis común daqueles que afectan ao sono. Afecta a un rango do 3% ao 7% da poboación con consecuencias severas sobre a saúde. O diagnóstico pasa pola realización dunha análise polisomnográfica (PSG) nunha Unidade do Sono dun centro hospitalario. A análise manual da devandita proba resulta moi custosa en tempo e esforzo para o médico especialista, e como consecuencia nun elevado custo económico. O sistema desenvolvido leva a cabo a análise automática do PSG dende unha perspectiva integral. A tal efecto, primeiro realizase unha análise dos sinais neurofisiolóxicos vinculados ao sono para obter o hipnograma, e seguidamente, lévase a cabo unha análise neumolóxica dos sinais respiratorios interpretándoos no contexto que marcan os demais sinais do PSG. Para leva a cabo esta tarefa o sistema apoiarase no uso de distintas técnicas de intelixencia artificial, con especial atención a mecanismos de razoamento con soporte para a imprecisión. O principal obxectivo do sistema proposto é a mellora do procedemento diagnóstico e axudar aos médicos no diagnóstico do SAHS

Deep learning for automated sleep monitoring

Wearable electroencephalography (EEG) is a technology that is revolutionising the longitudinal monitoring of neurological and mental disorders, improving the quality of life of patients and accelerating the relevant research. As sleep disorders and other conditions related to sleep quality affect a large part of the population, monitoring sleep at home, over extended periods of time could have significant impact on the quality of life of people who suffer from these conditions. Annotating the sleep architecture of patients, known as sleep stage scoring, is an expensive and time-consuming process that cannot scale to a large number of people. Using wearable EEG and automating sleep stage scoring is a potential solution to this problem. In this thesis, we propose and evaluate two deep learning algorithms for automated sleep stage scoring using a single channel of EEG. In our first method, we use time-frequency analysis for extracting features that closely follow the guidelines that human experts follow, combined with an ensemble of stacked sparse autoencoders as our classification algorithm. In our second method, we propose a convolutional neural network (CNN) architecture for automatically learning filters that are specific to the problem of sleep stage scoring. We achieved state-of-the-art results (mean F1-score 84%; range 82-86%) with our first method and comparably good results with the second (mean F1-score 81%; range 79-83%). Both our methods effectively account for the skewed performance that is usually found in the literature due to sleep stage duration imbalance. We propose a filter analysis and visualisation methodology for CNNs to understand the filters that CNNs learn. Our results indicate that our CNN was able to robustly learn filters that closely follow the sleep scoring guidelines.Open Acces

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

Analysis of sleep stage transitions and network physiology of control and sleep apnea subjects

Sleep is a naturally occurring neurological state of the human body that helps restore and regenerate physiological and mental systems. Sleep comprises of AWAKE, Non-rapid Eye Movement (NREM), and Rapid Eye Movement (REM) stages. NREM sleep consists of four sleep stages N1, N2, N3, and N4. In an ideal sleep cycle, a human subject transitions through the sleep stages in the order AWAKE -> N1 -> N2 -> N3 -> N4 -> REM. Even though sleep is a resting state of the human body, physiological systems like the central nervous system, the cardiac system, and the respiratory system are still working in their vegetative state. However, the impact of sleep pathologies like sleep apnea on sleep stage transitions and connectivity between physiological systems during sleep remains largely unknown. This research presents a four-phased methodology to identify differences in sleep stage transition patterns and connectivity between physiological systems between control and sleep apnea subjects during sleep. The analysis is performed on polysomnography and histogram data collected from the Sleep Heart Health Study (SHHS) dataset. In phase I, the frequently occurring sleep stage transition patterns in healthy and unhealthy subjects are identified using the Apriori algorithm. In phase II, we studied the coupling strength and coupling direction between time series signals of brain wave activities measured as EEG waves in the δ, θ, α, σ, β, ɣ1, and ɣ2 bands. We proposed a framework that implements the Time Delay Stability (TDS) method that identifies the coupling strength between EEG bands and the LSTM-based Granger Causality (LSTMGC) estimation method that determines the coupling direction of the identified links. The results show a high coupling strength in control subjects in all sleep stages compared to sleep apnea subjects. Most links are bidirectional in the awake stage for control and sleep apnea subjects. However, in other sleep stages, more unidirectional links are identified in sleep apnea subjects, indicating a reduced coupling between EEG bands. In phase III, we developed an LSTM-based conditional Granger causality (LSTMCGC) method to identify the indirect influences of oxygen saturation ('sao2') and nasal airflow ('airflow') on brain-heart interactions during sleep. The results indicate that during light sleep, the sao2 and airflow signals have a low influence on brain-heart interactions in sleep apnea subjects but strongly influence the control subjects. In the REM sleep stage, the sao2 and airflow signals strongly influence brain-heart interactions for sleep apnea subjects and have a low influence for control subjects. In phase IV, we developed the Change in Causation during Sleep (CCS) model to study the changes in causation between physiological systems during an 8-hour long sleep. We mainly studied the causation between heart rate ('hr') and oxygen saturation signals. The overall results indicate a high causality from sao2 to hr signals in the REM sleep stage for sleep apnea subjects. But no such association is observed for healthy subjects

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Introduction: Sleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques. Methods: Sleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis. Results: Neighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30 and 89.93 accuracy, respectively. Discussion and Conclusion: Similar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders. © 2018 Alizadeh Savareh et al

Developing Robust Models, Algorithms, Databases and Tools With Applications to Cybersecurity and Healthcare

As society and technology becomes increasingly interconnected, so does the threat landscape. Once isolated threats now pose serious concerns to highly interdependent systems, highlighting the fundamental need for robust machine learning. This dissertation contributes novel tools, algorithms, databases, and models—through the lens of robust machine learning—in a research effort to solve large-scale societal problems affecting millions of people in the areas of cybersecurity and healthcare. (1) Tools: We develop TIGER, the first comprehensive graph robustness toolbox; and our ROBUSTNESS SURVEY identifies critical yet missing areas of graph robustness research. (2) Algorithms: Our survey and toolbox reveal existing work has overlooked lateral attacks on computer authentication networks. We develop D2M, the first algorithmic framework to quantify and mitigate network vulnerability to lateral attacks by modeling lateral attack movement from a graph theoretic perspective. (3) Databases: To prevent lateral attacks altogether, we develop MALNET-GRAPH, the world’s largest cybersecurity graph database—containing over 1.2M graphs across 696 classes—and show the first large-scale results demonstrating the effectiveness of malware detection through a graph medium. We extend MALNET-GRAPH by constructing the largest binary-image cybersecurity database—containing 1.2M images, 133×more images than the only other public database—enabling new discoveries in malware detection and classification research restricted to a few industry labs (MALNET-IMAGE). (4) Models: To protect systems from adversarial attacks, we develop UNMASK, the first model that flags semantic incoherence in computer vision systems, which detects up to 96.75% of attacks, and defends the model by correctly classifying up to 93% of attacks. Inspired by UNMASK’s ability to protect computer visions systems from adversarial attack, we develop REST, which creates noise robust models through a novel combination of adversarial training, spectral regularization, and sparsity regularization. In the presence of noise, our method improves state-of-the-art sleep stage scoring by 71%—allowing us to diagnose sleep disorders earlier on and in the home environment—while using 19× less parameters and 15×less MFLOPS. Our work has made significant impact to industry and society: the UNMASK framework laid the foundation for a multi-million dollar DARPA GARD award; the TIGER toolbox for graph robustness analysis is a part of the Nvidia Data Science Teaching Kit, available to educators around the world; we released MALNET, the world’s largest graph classification database with 1.2M graphs; and the D2M framework has had major impact to Microsoft products, inspiring changes to the product’s approach to lateral attack detection.Ph.D