Autonomic Nervous System and Rem Behavior Sleep Disorder: a new tool to identify Idiopathic or Parkinsonians patients through Heart Rate Variability Polysomnography Analysis

Objective: RBD is a sleep disorder known to be associated in a very high percentage of cases with alfa-synucleopathies. During rapid eye movement (REM) sleep, the cardiovascular system is unstable and greatly influenced by sympathetic activity. Heart Rate Variability (HRV) indirectly tests functions and activities of the ANS during sleep. We evaluate whether HRV and Hypnogram Indices are polysomnographic valid biomarkers to distinguish subjects with idiopathic RBD from those with Parkinson's Disease. METHODS: Our study examines HRV linear and non-linear indices of 37 patients aged 53 years and older (median 72.7; mean 72.3 ± 7.4; range 53-84), 7 women (18.9%) and 30 men (81.1%). 22 pts were idiopathic REM sleep behavior disorder (59.5%; age: median 72.5; mean 74.5 ± 5.2; range 68 83), of which 3 women (13.6%) and 19 men (86.4%); 15 pts had REM Sleep Behavior Disorder secondary to Parkinson's disease (40.5%; a ge: median 73; mean 69.5 ± 9 ; range 53 84), including 4 women (26.7%) and 11 men (73.3%). A parallel Analysis was made on Hypnogram Parameters. RESULTS: The REM sleep phase allowed to record the greatest number of significant differences in HRV Index between the two groups of patients. Among the Frequency HRV Linear Indices, VLF signal band recorded the highest number of significant results suggesting that the sympathetic component may be the one most compromised in the autonomic neurodegeneration process of RBD. HRV Complexity Non-Linear Indices (LZC and KC) have the highest number of statistically significant results so that could be the right parameter to use to distinguish our two RBD populations. Hypnogram Indices Analysis showed no significant value for not even a parameter. CONCLUSIONS: HRV can be a valid biomarker to distinguish the two populations of patients affected by RBD, both idiopathic and affected by Parkinson's disease. It could represent a new and easy tool to identify, among the REM Behavior Sleep Disorder, patients affected or not by Parkinson’s desease or could be even useful when an early diagnosis is needed or it is necessary monitoring a probable conversion from idiopathic form to PD or evaluate the effectiveness of RBD therapies

Impact of nonstationarities on short heart rate variability recordings during obstructive sleep apnea

Obstructive sleep apnea (OSA) is a sleep disorder characterized by breathing pauses due to collapse of the upper airways. During OSA the autonomic modulation, as noninvasively assessed through heart period (HP) variability, is altered in a time-varying way even though time-varying properties of HP fluctuations are often disregarded by HP variability studies. We performed a time domain analysis computed over very short epochs corresponding to the sole OSA events explicitly accounting for HP variability nonstationarities. Length-matched epochs were extracted during OSA and quiet sleep (SLEEP) in 13 subjects suffering from OSA (11 males, age 55±11, apnea-hypopnea index 44±19). Mean HP, variance and variance of the residual after exponential detrending were assessed as well as the parameters a and b of the exponential fitting in the form y(n)=a·exp(b·n). HP mean and the parameter a increased during OSA compared to SLEEP. The variance of the residual was significantly lower than original variance during both OSA and SLEEP, while the dispersion of the parameter b was significantly larger. Nonstationarities were evident during both SLEEP and OSA but more dramatically apparent during OSA, thus stressing the need of accounting for them when the autonomic control during OSA is under scrutiny

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

IoT-Based Wireless Polysomnography Intelligent System for Sleep Monitoring

© 2013 IEEE. Polysomnography (PSG) is considered the gold standard in the diagnosis of obstructive sleep apnea (OSA). The diagnosis of OSA requires an overnight sleep experiment in a laboratory. However, due to limitations in relation to the number of labs and beds available, patients often need to wait a long time before being diagnosed and eventually treated. In addition, the unfamiliar environment and restricted mobility when a patient is being tested with a polysomnogram may disturb their sleep, resulting in an incomplete or corrupted test. Therefore, it is posed that a PSG conducted in the patient's home would be more reliable and convenient. The Internet of Things (IoT) plays a vital role in the e-Health system. In this paper, we implement an IoT-based wireless polysomnography system for sleep monitoring, which utilizes a battery-powered, miniature, wireless, portable, and multipurpose recorder. A Java-based PSG recording program in the personal computer is designed to save several bio-signals and transfer them into the European data format. These PSG records can be used to determine a patient's sleep stages and diagnose OSA. This system is portable, lightweight, and has low power-consumption. To demonstrate the feasibility of the proposed PSG system, a comparison was made between the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system. Several healthy volunteer patients participated in the PSG experiment and were monitored by both the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system simultaneously, under the supervision of specialists at the Sleep Laboratory in Taipei Veteran General Hospital. A comparison of the results of the time-domain waveform and sleep stage of the two systems shows that the proposed system is reliable and can be applied in practice. The proposed system can facilitate the long-Term tracing and research of personal sleep monitoring at home

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

Using machine learning for wearables to understand the association of sleep with future morbidity

Sleep is essential to life and is structurally complex. Our understanding of how sleep is associated with health and morbidity primarily draws on studies that use self-report sleep diaries, which capture the subjective experience. However, sleep diaries are limited because they are measured at one point in time, single dimensional, often only capturing sleep duration, and have a low correlation with objective device-measured sleep parameters. The accepted standard for sleep measurement is laboratory-based polysomnography, which is not feasible for use at scale due to its high cost and technical complexity. Instead, wrist-worn accelerometers are more viable to deploy in large-scale epidemiological studies because of their portability and low user burden. Therefore, I aimed to develop a machine learning method for sleep stage classification and evaluate its utility to provide insights into the association between sleep and health outcomes. I conducted a systematic review to assess the agreement between accelerometer-based sleep staging and polysomnography with a secondary aim of understanding the agreement level from different methods used. This review found that existing sleep staging methods were limited by a reliance on hand-crafted features and the use of labelled datasets of small sizes, highlighting the need to improve accelerometer-based sleep staging. A self-supervised deep neural network was first developed to automatically extract features from 700,000 person-days of unlabelled raw accelerometry. To systematically evaluate the generalisability of the self-supervised features, the pre-trained network was tested in seven human activity recognition datasets, for which more open-access benchmark datasets were available. The self-supervised network showed generalisability across activity classes, devices, device placements and populations with an F1 relative improvement of 2.5%-100% (median: 18.4%) compared to the network without self-supervision. The self-supervised feature extractor was then used to develop a sleep stage classifier (SleepNet) using a deep recurrent neural network. The SleepNet was able to obtain a state-of-the-art performance for the classification of sleep and the stages of sleep using ~1,500 nights of multi-centre polysomnography as the ground truth. Overall, the derived sleep parameters had a fair agreement with polysomnography with a Kappa score of 0.37 (SD: 0.16) for three-class classification between wake, rapid-eye-movement sleep (REM), and non-rapid-eye-movement sleep (NREM). The difference between polysomnography and the model classifications on the external validation was 34.7 minutes (95% limits of agreement (LoA): -37.8 to 107.2 minutes) for total sleep duration, 2.6 minutes for REM duration (95% LoA: -68.4 to 73.4 minutes) and 32.1 minutes (95% LoA: -54.4 to 118.5 minutes) for NREM duration. Finally, SleepNet was used to infer overnight sleep duration and sleep efficiency (the proportion of time asleep when in bed) in the UK Biobank accelerometer dataset to understand the associations with mortality outcomes. Short sleepers (<6 hours) had a higher risk of mortality compared to participants with normal sleep duration 6 to 7.9 hours, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.58; 95% confidence intervals (CIs): 1.19 to 2.11) or high sleep efficiency (HRs: 1.45; 95% CIs: 1.16 to 1.81). In conclusion, accelerometer-based sleep classification had a fair agreement with polysomnography. Using the derived sleep parameters on datasets with longitudinal follow-up could transform our understanding of how sleep contributes to human health and well-being

Automated sleep stage detection and classification of sleep disorders

Studies have demonstrated that more than 1 million Australians experience some sort of sleep-related disorder in their lifetime [12]. In order to improve the diagnostic and clinical treatment of sleep disorders, the first important step is to identify or automatically detect the sleep stages. The most common method, known as the visual sleep stage scoring, can be a tedious and time-consuming process. Because of that, there is a need to create or develop an improved automatic sleep stage detection method to assist the sleep physician to efficiently and accurately evaluate the sleep stages of patients or non-patients. This research project consisted of two parts. The first part focused on the automatic sleep stages detection based on two individual bio-signals, which made up an overnight polysomnography (PSG), such as the electroencephalogram (EEG), and electrooculogram (EOG). Several features were extracted from these two bio-signals in the time and frequency domains. The decision tree and classification methods were utilised for the classification of the sleep stages. The second part of this project focused on the automatic classification of different sleep and psychiatric disorders, such as patients with periodic limb movements of sleep (PLMs), sleep apnea-hypopnea syndrome (SAHS), primary insomnia, schizophrenia and healthy sleep. Different PSG parameters were computed for the classification of sleep disorders, such as descriptive statistics of sleep architecture. In conclusion, the advantage of an automatic sleep stage detection method based on a single-channel EEG or EOG signal can be undertaken with portable sleep stage recording instead of full the PSG system, which includes multichannel bio-signals. An automatic classification method of sleep and psychiatric disorders based on the descriptive statistics of sleep architecture statistics was found to be an effective technique for screening sleep and psychiatric disorders. This classification method can assist physicians to quickly undertake a diagnostic procedure