A review of automated sleep disorder detection

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand

Towards an artificial therapy assistant: Measuring excessive stress from speech

The measurement of (excessive) stress is still a challenging endeavor. Most tools rely on either introspection or expert opinion and are, therefore, often less reliable or a burden on the patient. An objective method could relieve these problems and, consequently, assist diagnostics. Speech was considered an excellent candidate for an objective, unobtrusive measure of emotion. True stress was successfully induced, using two storytelling\ud sessions performed by 25 patients suffering from a stress disorder. When reading either a happy or a sad story, different stress levels were reported using the Subjective Unit of Distress (SUD). A linear regression model consisting of the high-frequency energy, pitch, and zero crossings of the speech signal was able to explain 70% of the variance in the subjectively reported stress. The results demonstrate the feasibility of an objective measurement of stress in speech. As such, the foundation for an Artificial Therapeutic Agent is laid, capable of assisting therapists through an objective measurement of experienced stress

Sleep heart rate variability analysis and k-nearest neighbours classification of primary insomnia

The Heart Rate Variability (HRV) of many sleep disorders shows an alteration of the sympathovagal balance of the Autonomous Nervous System (ANS). Primary insomnia refers to the difficulty in initiating or maintaining sleep that is not caused by other illnesses or substances. The HRV of primary insomnia shows inconsistent findings although it is believed to impair the HRV variables. This study compares the HRV changes during different sleep stages and evaluates the k-nearest neighbours (kNN) classifier using the HRV features for primary insomnia classification. The time and frequency HRV variables were extracted from sleep ECG signals of 10 primary insomnia patients and 10 healthy controls during four sleep stages - N1, N2, N3 and REM. The Mann-Whitney U-test was conducted to evaluate the existence of statistical significant differences between the two groups at different sleep stages. The kNN classifier was adapted for the classification tool. Only the LF index of HRV was significantly higher in the primary insomnia patients compared to the healthy subjects. The classification accuracy of kNN was at 75% when both the HRV time and frequency variables were accounted as inputs to the classifier

Dheergayu: Clinical Depression Monitoring Assistant

Depression is identified as one of the most common mental health disorders in the world. Depression not only impacts the patient but also their families and relatives. If not properly treated, due to these reasons it leads people to hazardous situations. Nonetheless existing clinical diagnosis tools for monitoring illness trajectory are inadequate. Traditionally, psychiatrists use one to one interaction assessments to diagnose depression levels. However, these cliniccentered services can pose several operational challenges. In order to monitor clinical depressive disorders, patients are required to travel regularly to a clinical center within its limited operating hours. These procedures are highly resource intensive because they require skilled clinician and laboratories. To address these issues, we propose a personal and ubiquitous sensing technologies, such as fitness trackers and smartphones, which can monitor human vitals in an unobtrusive manner

Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control

The application of complex systems theory to physiology and medicine has provided meaningful information about the nonlinear aspects underlying the dynamics of a wide range of biological processes and their disease-related aberrations. However, no studies have investigated whether meaningful information can be extracted by quantifying second-order moments of time-varying cardiovascular complexity. To this extent, we introduce a novel mathematical framework termed complexity variability, in which the variance of instantaneous Lyapunov spectra estimated over time serves as a reference quantifier. We apply the proposed methodology to four exemplary studies involving disorders which stem from cardiology, neurology and psychiatry: Congestive Heart Failure (CHF), Major Depression Disorder (MDD), Parkinson?s Disease (PD), and Post-Traumatic Stress Disorder (PTSD) patients with insomnia under a yoga training regime. We show that complexity assessments derived from simple time-averaging are not able to discern pathology-related changes in autonomic control, and we demonstrate that between-group differences in measures of complexity variability are consistent across pathologies. Pathological states such as CHF, MDD, and PD are associated with an increased complexity variability when compared to healthy controls, whereas wellbeing derived from yoga in PTSD is associated with lower time-variance of complexity

A review of automated sleep stage scoring based on physiological signals for the new millennia

Background and Objective: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal. Methods: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. Results: Our review shows that all of these signals contain information for sleep stage scoring. Conclusions: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost

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

Salivary melatonin onset in youth at familial risk for bipolar disorder

Melatonin secretion and polysomnography (PSG) were compared among a group of healthy adolescents who were at high familial risk for bipolar disorder (HR) and a second group at low familial risk (LR). Adolescent participants (n = 12) were a mean age 14 ± 2.3 years and included 8 females and 4 males. Saliva samples were collected under standardized condition light (red light) and following a 200 lux light exposure over two consecutive nights in a sleep laboratory. Red Light Melatonin onset (RLMO) was defined as saliva melatonin level exceeding the mean of the first 3 readings plus 2 standard deviations. Polysomnography was also completed during each night. HR youth, relative to LR, experienced a significantly earlier melatonin onset following 200 lux light exposure. Polysomnography revealed that LR youth, relative to HR, spent significantly more time in combined stages 3 and 4 (deep sleep) following red light exposure. Additionally, regardless of the group status (HR or LR), there was no significant difference in Red Light Melatonin Onset recorded at home or in the laboratory, implying its feasibility and reliability

Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep