Using dynamic time warping for sleep and wake discrimination

In previous work, a Linear Discriminant (LD) classifier was used to classify sleep and wake states during single-night polysomnography recordings (PSG) of actigraphy, respiratory effort and electrocardiogram (ECG). In order to improve the sleep-wake discrimination performance and to reduce the number of modalities needed for class discrimination, this study incorporated Dynamic Time Warping (DTW) to help discriminate between sleep and wake states based on actigraphy and respiratory effort signal. DTW quantifies signal similarities manifested in the features extracted from the respiratory effort signal. Experiments were conducted on a dataset acquired from nine healthy subjects, using an LD-based classifier. Leave-one- out cross-validation shows that adding this DTW-based feature to the original actigraphy- and respiratory-based feature set results in an epoch-by-epoch Cohen’s Kappa agreement coefficient of ¿ = 0.69 (at an overall accuracy of 95.4%), which represents a significant improvement when compared with the performance obtained without using this feature. Furthermore it is comparable to the result obtained in the previous work which used additional ECG features (¿ = 0.70)

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

Validation of actigraphy for sleep measurement in children with cerebral palsy

OBJECTIVES: Sleep issues are common in children with cerebral palsy (CP), although there are challenges in obtaining objective data about their sleep patterns. Actigraphs measure movement to quantify sleep but their accuracy in children with CP is unknown. Our goals were to validate actigraphy for sleep assessment in children with CP and to study their sleep patterns in a cross-sectional cohort study. METHODS: We recruited children with (N = 13) and without (N = 13) CP aged 2-17 years (mean age 9 y 11mo [SD 4 y 10mo] range 4-17 y; 17 males, 9 females; 54% spastic quadriplegic, 23% spastic diplegic, 15% spastic hemiplegic, 8% unclassified CP). We obtained wrist and forehead actigraphy with concurrent polysomnography for one night, and home wrist actigraphy for one week. We developed actigraphy algorithms and evaluated their accuracy (agreement with polysomnography-determined sleep versus wake staging), sensitivity (sleep detection), and specificity (wake detection). RESULTS: Our actigraphy algorithms had median 72-80% accuracy, 87-91% sensitivity, and 60-71% specificity in children with CP and 86-89% accuracy, 88-92% sensitivity, and 70-75% specificity in children without CP, with similar accuracies in wrist and forehead locations. Our algorithms had increased specificity and accuracy compared to existing algorithms, facilitating detection of sleep disruption. Children with CP showed lower sleep efficiency and duration than children without CP. CONCLUSIONS: Actigraphy is a valid tool for sleep assessment in children with CP. Children with CP have worse sleep efficiency and duration

System and method for cardiorespiratory sleep stage classification

The present disclosure pertains to a system configured to determine one or more parameters based on cardiorespiratory information from a subject and determine sleep stage classifications based on a discriminative undirected probabilistic graphical model such as Conditional Random Fields using the determined parameters. The system is advantageous because sleep is a structured process in which parameters determined for individual epochs are not independent over time and the system determines the sleep stage classifications based on parameters determined for a current epoch, determined relationships between parameters, sleep stage classifications determined for previous epochs, and/or other information. The system does not assume that determined parameters are discriminative during an entire sleep stage, but maybe indicative of a sleep stage transition alone. In some embodiments, the system comprises one or more sensors, one or more physical computer processors, electronic storage, and a user interface

Sleep stage classification based on cardiorespiratory signals

Dissertação de mestrado em Engenharia InformáticaO sono está ligado a uma quantidade bastante considerável de patologias que têm impacto direto na maioria das atividades diárias tais como a aprendizagem, memorização ou produtividade. Assim, reduzir as consequências pessoais e os custos associados com os distúrbios do sono tornou-se num dos maiores desafios das últimas décadas. Patologias como distúrbios respiratórios do sono, sonolência, síndrome de pernas inquietas ou distúrbios do sono relacionados com o ritmo circadiano são bastante prevalentes, produzindo grandes distúrbios no dia-a-dia dos pacientes. Para o diagnóstico e tratamento deste tipo de patologias, a capacidade de avaliar o padrão de sono do paciente por períodos de tempo mais alargados poderá ser necessária. A necessidade de avaliação de um determinado medicamento ou a monitorização da qualidade do sono do paciente ao longo do tempo são bons exemplos. O teste clínico PSG, atualmente padrão para a avaliação do sono, é um método caro e complexo, disponíveis apenas em hospitais especializados e equipados com um laboratório do sono e profissionais qualificados. Para além de nem sempre estar disponível, PSG é considerado um procedimento muito penoso devido aos diversos elétrodos em contacto com o corpo e cabeça, que causam desconforto e possivelmente um padrão de sono anormal. Para além destes incómodos, os pacientes têm ainda que dormir num laboratório, sendo continuamente observados ao longo da noite. PSG é, portanto, uma técnica cara, geralmente limitada a uma ou duas noites num laboratório do sono. Métodos como actigraphy, que utilizam sensores semelhantes a relógios de pulso para medir os movimentos corporais dos pacientes, podem dar informações úteis sobre os padrões de sono dos indivíduos durante períodos de tempo mais alargados sem perturbar significativamente os hábitos normais de sono dos pacientes. No entanto, este método tem várias limitações, uma vez que apenas avalia movimentos corporais, o que é insuficiente para informações relativas à arquitetura do sono dos pacientes. Para ultrapassar as limitações dos métodos acima descritos, seria relevante a criação de um novo procedimento capaz de complementar os já existentes. Um sistema de monitorização do sono baseado em informação cardiorrespiratória poderá fornecer mais informação sobre a arquitetura do sono, de forma não intrusiva e durante períodos de tempo alargados, no conforto e privacidade da residência dos pacientes. Esta informação poderia ser utilizada para o rastreio de doenças, acompanhamento e monitorização de tratamentos ou mesmo complementar o PSG para o diagnóstico de algumas doenças do sono. O sistema apresentado neste trabalho aborda parte desta hipótese, classificando automaticamente várias fases do sono usando apenas informação cardiorrespiratória. Embora os dados utilizados para este estudo, tenham sido adquiridos através do uso de sensores de contacto, no futuro, esta informação poderá ser obtida através da utilização de métodos não intrusivos, que já se encontram disponíveis comercialmente. Esta hipótese é bastante interessante porque consegue fornecer mais informação aos profissionais do sono, sem interferir com o dia-a-dia do paciente.Sleep pathologies have a direct negative impact into most of daily activities such has learning, memorization or productivity. Decreasing the personal burden and the societal cost associated with sleep disturbances has become one of the major challenges in the last decades. Pathologies like sleep disordered breathing, insomnia, restless leg syndrome or circadian rhythm sleep disorders are fairly prevalent, heavily disturbing the life of af-fected subjects. For the diagnosis and treatment of these disorders, the ability to assess a patient’s sleep pattern over longer periods of time may be required. The need of evalua-tion of a certain medication or the monitoring of the sleep quality of the patient over time can be named as good examples. The polysomonographic (PSG) clinical test, current gold standard for sleep assessment, is an expensive and complex method only available in specialized hospitals equipped with a sleep lab and qualified professionals. Not always available, PSG is considered a very stressful procedure because of the various electrodes attached to the body and head, which cause discomfort and potentially disrupt the usual sleep patterns. Furthermore people need to sleep in an unfamiliar environment while be-ing observed throughout the entire night. PSG is therefore an expensive technique usually limited to one or two nights in a sleep laboratory. Methods like actigraphy, which measure body movements, can give useful insight about the sleeping patterns of the subjects during longer periods of time without significantly disrupting the normal sleeping habits of a person. However this method has several limitations as it only assesses the movements of the patients and therefore provides little insight about the subjects’ sleep architecture. In order to address the shortcomings of the existing techniques, the introduction of a new system, easy and cheap to deploy and use, capable of complementing the existent ap-proaches is relevant. A sleep monitoring system based on cardiorespiratory data may be able to provide bigger insight of the sleep architecture, while having the potential to be unobtrusive and able to monitor sleep during longer periods of time, in the comfort and privacy of the subject’s own room. Furthermore it can potentially enable the screening of diseases, follow-up on treatments, or even complementing PSG for diagnosis of some sleep disorders. The system presented in this work addresses part of this system by automatically classi-fying multiple sleep stages using cardiorespiratory information. Although the data used for this study was acquired with contact sensors, in the future, this information might be obtained through the use of non-obtrusive methods that are already commercially availa-ble. This possibility is interesting as it provides bigger insight of the subject sleeping patterns and architecture for the sleep professional, without interfering with the daily life of the patient

Using Capacitance Sensor to Extract Characteristic Signals of Dozing from Skin Surface

Skin is the largest organ of the human body and a physiological structure that is directly exposed to the environment. From a theoretical perspective, numerous physiological and psychological signals use the skin as a medium for input and output with the outside world. Therefore, the skin is considered an optimal signal interception point when developing noninvasive, direct, and rapid signal exploration devices. To date, skin signal interceptions are predominantly performed by measuring skin impedance. However, this method is prone to interference such as sweat secretion, salt accumulation on the skin, and muscle contractions, which may result in a substantial amount of interference and erroneous results. The present study proposes novel and effective methods for skin signal interception, such as using a nested probe as a sensor to measure capacitance to be further processed as physiological and psychological signals. The experimental results indicate that the capacitance curve for the transition between wakefulness and dozing exhibits significant changes. This change in the curve can be analyzed by computer programs to clearly and rapidly determine whether the subject has entered the initial phases of sleep