Self-advising SVM for sleep apnea classification

In this paper Self-Advising SVM, a new proposed version of SVM, is investigated for sleep apnea classification. Self-Advising SVM tries to trans-fer more information from training phase to the test phase in compare to the traditional SVM. In this paper Sleep apnea events are classified to central, ob-structive or mixed, by using just three signals, airflow, abdominal and thoracic movement, as inputs. Statistical tests show that self-advising SVM performs better than traditional SVM in sleep apnea classification

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

Wearable Sleep Technology in Clinical and Research Settings

The accurate assessment of sleep is critical to better understand and evaluate its role in health and disease. The boom in wearable technology is part of the digital health revolution and is producing many novel, highly sophisticated and relatively inexpensive consumer devices collecting data from multiple sensors and claiming to extract information about users' behaviors, including sleep. These devices are now able to capture different biosignals for determining, for example, HR and its variability, skin conductance, and temperature, in addition to activity. They perform 24/7, generating overwhelmingly large data sets (big data), with the potential of offering an unprecedented window on users' health. Unfortunately, little guidance exists within and outside the scientific sleep community for their use, leading to confusion and controversy about their validity and application. The current state-of-the-art review aims to highlight use, validation and utility of consumer wearable sleep-trackers in clinical practice and research. Guidelines for a standardized assessment of device performance is deemed necessary, and several critical factors (proprietary algorithms, device malfunction, firmware updates) need to be considered before using these devices in clinical and sleep research protocols. Ultimately, wearable sleep technology holds promise for advancing understanding of sleep health; however, a careful path forward needs to be navigated, understanding the benefits and pitfalls of this technology as applied in sleep research and clinical sleep medicine

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

Analysis of EEG signals using complex brain networks

The human brain is so complex that two mega projects, the Human Brain Project and the BRAIN Initiative project, are under way in the hope of answering important questions for peoples' health and wellbeing. Complex networks become powerful tools for studying brain function due to the fact that network topologies on real-world systems share small world properties. Examples of these networks are the Internet, biological networks, social networks, climate networks and complex brain networks. Complex brain networks in real time biomedical signal processing applications are limited because some graph algorithms (such as graph isomorphism), cannot be solved in polynomial time. In addition, they are hard to use in single-channel EEG applications, such as clinic applications in sleep scoring and depth of anaesthesia monitoring. The first contribution of this research is to present two novel algorithms and two graph models. A fast weighted horizontal visibility algorithm (FWHVA) overcoming the speed limitations for constructing a graph from a time series is presented. Experimental results show that the FWHVA can be 3.8 times faster than the Fast Fourier Transfer (FFT) algorithm when input signals exceed 4000 data points. A linear time graph isomorphism algorithm (HVGI) can determine the isomorphism of two horizontal visibility graphs (HVGs) in a linear time domain. This is an efficient way to measure the synchronized index between two time series. Difference visibility graphs (DVGs) inherit the advantages of horizontal visibility graphs. They are noise-robust, and they overcome a pitfall of visibility graphs (VG): that the degree distribution (DD) doesn't satisfy a pure power-law. Jump visibility graphs (JVGs) enhance brain graphs allowing the processing of non-stationary biomedical signals. This research shows that the DD of JVGs always satisfies a power-lower if the input signals are purely non-stationary. The second highlight of this work is the study of three clinical biomedical signals: alcoholic, epileptic and sleep EEGs. Based on a synchronization likelihood and maximal weighted matching method, this work finds that the processing repeated stimuli and unrepeated stimuli in the controlled drinkers is larger than that in the alcoholics. Seizure detections based on epileptic EEGs have also been investigated with three graph features: graph entropy of VGs, mean strength of HVGs, and mean degrees of JVGs. All of these features can achieve 100% accuracy in seizure identification and differentiation from healthy EEG signals. Sleep EEGs are evaluated based on VG and DVG methods. It is shown that the complex brain networks exhibit more small world structure during deep sleep. Based on DVG methods, the accuracy peaks at 88:9% in a 5-state sleep stage classification from 14; 943 segments from single-channel EEGs. This study also introduces two weighted complex network approaches to analyse the nonlinear EEG signals. A weighted horizontal visibility graph (WHVG) is proposed to enhance noise-robustness properties. Tested with two Chaos signals and an epileptic EEG database, the research shows that the mean strength of the WHVG is more stable and noise-robust than those features from FFT and entropy. Maximal weighted matching algorithms have been applied to evaluate the difference in complex brain networks of alcoholics and controlled drinkers. The last contribution of this dissertation is to develop an unsupervised classifier for biomedical signal pattern recognition. A Multi-Scale Means (MSK-Means) algorithm is proposed for solving the subject-dependent biomedical signals classification issue. Using JVG features from the epileptic EEG database, the MSK-Means algorithm is 4:7% higher in identifying seizures than those by the K-means algorithm and achieves 92:3% accuracy for localizing the epileptogenic zone. The findings suggest that the outcome of this thesis can improve the performance of complex brain networks for biomedical signal processing and nonlinear time series analysis

Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach

IntroductionAlzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers.MethodsMild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers.ResultsOn 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors.ConclusionsMultimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions

Artificial intelligence techniques for studying neural functions in coma and sleep disorders

The use of artificial intelligence in computational neuroscience has increased within the last years. In the field of electroencephalography (EEG) research machine and deep learning, models show huge potential. EEG data is high dimensional, and complex models are well suited for their analysis. However, the use of artificial intelligence in EEG research and clinical applications is not yet established, and multiple challenges remain to be addressed. This thesis is focused on analyzing neurological EEG signals for clinical applications with artificial intelligence and is split into three sub-projects. The first project is a methodological contribution, presenting a proof of concept that deep learning on EEG signals can be used as a multivariate pattern analysis technique for research. Even though the field of deep learning for EEG has produced many publications, the use of these algorithms in research for the analysis of EEG signals is not established. Therefore for my first project, I developed an analysis pipeline based on a deep learning architecture, data augmentation techniques, and feature extraction method that is class and trial-specific. In summary, I present a novel multivariate pattern analysis pipeline for EEG data based on deep learning that can extract in a data-driven way trial-by-trial discriminant activity. In the second part of this thesis, I present a clinical application of predicting the outcome of comatose patients after cardiac arrest. Outcome prediction of patients in a coma is today still an open challenge, that depends on subjective clinical evaluations. Importantly, current clinical markers can leave up to a third of patients without a clear prognosis. To address this challenge, I trained a convolutional neural network on EEG signals of coma patients that were exposed to standardized auditory stimulations. This work showed a high predictive power of the trained deep learning model, also on patients that were without a established prognosis based on existing clinical criteria. These results emphasize the potential of deep learning models for predicting outcome of coma and assisting clinicians. In the last part of my thesis, I focused on sleep-wake disorders and studied whether unsupervised machine learning techniques could improve diagnosis. The field of sleep-wake disorders is convoluted, as they can cooccur within patients, and only a few disorders have clear diagnostic biomarkers. Thus I developed a pipeline based on an unsupervised clustering algorithm to disentangle the full landscape of sleep-wake disorders. First I reproduced previous results in a sub-cohort of patients with central disorders of hypersomnolence. The verified pipeline was then used on the full landscape of sleep-wake disorders, where I identified clear clusters of disorders with clear diagnostic biomarkers. My results call for new biomarkers, to improve patient phenotyping

Analisi dei parametri di risposta cerebrale e vegetativa agli eventi respiratori nel sonno in pazienti affetti da sindrome delle apnee morfeiche

The arousal scoring in Obstructive Sleep Apnea Syndrome (OSAS) is important to clarify the impact of the disease on sleep but the currently applied American Academy of Sleep Medicine (AASM) definition may underestimate the subtle alterations of sleep. The aims of the present study were to evaluate the impact of respiratory events on cortical and autonomic arousal response and to quantify the additional value of cyclic alternating pattern (CAP) and pulse wave amplitude (PWA) for a more accurate detection of respiratory events and sleep alterations in OSAS patients. A retrospective revision of 19 polysomnographic recordings of OSAS patients was carried out. Analysis was focused on quantification of apneas (AP), hypopneas (H) and flow limitation (FL) events, and on investigation of cerebral and autonomic activity. Only 41.1% of FL events analyzed in non rapid eye movement met the AASM rules for the definition of respiratory event-related arousal (RERA), while 75.5% of FL events ended with a CAP A phase. The dual response (EEG-PWA) was the most frequent response for all subtypes of respiratory event with a progressive reduction from AP to H and FL. 87.7% of respiratory events with EEG activation showed also a PWA drop and 53,4% of the respiratory events without EEG activation presented a PWA drop. The relationship between the respiratory events and the arousal response is more complex than that suggested by the international classification. In the estimation of the response to respiratory events, the CAP scoring and PWA analysis can offer more extensive information compared to the AASM rules. Our data confirm also that the application of PWA scoring improves the detection of respiratory events and could reduce the underestimation of OSAS severity compared to AASM arousal