813 research outputs found

    Analyzing sensor based human activity data using time series segmentation to determine sleep duration

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    Sleep is the most important thing to rest our brain and body. A lack of sleep has adverse effects on overall personal health and may lead to a variety of health disorders. According to Data from the Center for disease control and prevention in the United States of America, there is a formidable increase in the number of people suffering from sleep disorders like insomnia, sleep apnea, hypersomnia and many more. Sleep disorders can be avoided by assessing an individual\u27s activity over a period of time to determine the sleep pattern and duration. The sleep pattern and duration can be determined for an individual with the help of commercially available fitness devices such as Fitbit, Nike, Apple, and many others, which are activity trackers with accelerometer sensors. But these devices determine sleep duration from a \u27Proprietary Algorithm\u27, which processes the movement sensor data. Due to the proprietary nature, in a long-term study, the developer of the algorithm could update and make changes to the algorithm without revealing the details of the update to the user. This affects the measures reported by the algorithm. Hence to determine correct and reliable sleep duration, an Algorithm is developed by directly analyzing the actigraphy signals using time series segmentation. The study was done on a group of 20 healthy Undergraduate students from Missouri University of Science and Technology, whose daily physical activities were recorded using the GENEActiv accelerometer wristwatch worn on the non-dominant wrist. In this thesis, an open source algorithm has been developed using the daily physical activity data to estimate the sleep duration for any individual --Abstract, page iii

    Sleep quality influences subsequent motor skill acquisition

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    While the influence of sleep on motor memory consolidation has been extensively investigated, its relation to initial skill acquisition is less well understood. The purpose of the present study was to investigate the influence of sleep quality and quantity on subsequent motor skill acquisition in young adults without sleep disorders. Fifty-five healthy adults (mean age = 23.8 years; 34 women) wore actigraph wristbands for 4 nights, which provided data on sleep patterns before the experiment, and then returned to the laboratory to engage in a motor sequence learning task (explicit 5-item finger sequence tapping task). Indicators of sleep quality and quantity were then regressed on a measure of motor skill acquisition (Gains Within Training, GWT). Wake After Sleep Onset (WASO; i.e., the total amount of time the participants spent awake after falling asleep) was significantly and negatively related to GWT. This effect was not because of general arousal level, which was measured immediately before the motor task. Conversely, there was no relationship between GWT and sleep duration or self-reported sleep quality. These results indicate that sleep quality, as assessed by WASO and objectively measured with actigraphy before the motor task, significantly impacts motor skill acquisition in young healthy adults without sleep disorders. (PsycINFO Database Record. (c) 2016 APA, all rights reserved).Accepted manuscrip

    Classification of Rest and Active Periods in Actigraphy Data using PCA

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    In this paper we highlight a clustering algorithm for the purpose of identifying sleep and wake periods directly from actigraphy signals. The paper makes use of statistical Principal Component Analysis to identify periods of rest and activity. The aim of the proposed methodology is to develop a quick and efficient method to determine the sleep duration of an individual. In addition, a robust method that can identify sleep periods in the accelerometer data when duration, time of day varies by individual. A selected group of 10 individual\u27s sensor data consisting of actigraphy from an accelerometer (3-axis), near body temperature, and lux sensors from a single GENEActiv watch worn on the non-dominant hand. The actigraphy of each individual was collected 24 hours a day for a period spanning 80 days. We highlight that a simple data preprocessing stage followed with a 2 phase clustering method provides results that align with previously validated methodologies

    Protocol of the SOMNIA project : an observational study to create a neurophysiological database for advanced clinical sleep monitoring

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    Introduction Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods. Methods and analysis We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm

    Wireless Sensor System for Mild Cognitive Impairment Diagnosis

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    Alzheimer’s disease (AD), which causes decline in the cognitive functions, is the major lead of dementia. AD begins showing damage in memory, making patients dependent on caregivers. Treating AD requires early diagnosis of its signs. The initial sign of AD is mild cognitive impairment (MCI), which is the middle stage between a healthy patient and one diagnosed with AD. The proposed sleep monitoring system is capable of diagnosing MCI symptoms. MCI patients are characterized with sleep fragmentation and sleep disorder. The sleep fragmentation is defined as awakenings that interrupt the normal sleep. The proposed system in this thesis uses force sensors to capture movements that can potentially characterize as sleep fragmentation. The sensors were arranged on a mattress cover to pick up the body movements regardless of sleep position. A wireless sensor system was designed, implemented and tested in Wireless Sensor Networks (WiSe-Net) laboratory at the University of Maine. The system operates at 2.4GHz with a range of 300ft. It has been tested on control subjects, healthy people, and seniors diagnosed with MCI and AD. The system is capable of determining the number and average periods of sleep fragmentation to assist with diagnosing MCI

    Automatic-Scoring Actigraph Compares Favourably to a Manually-Scored Actigraph for Sleep Measurement in Healthy Adults.

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    Introduction  Actigraphy has been used widely in sleep research due to its non-invasive, cost-effective ability to monitor sleep. Traditionally, manually-scored actigraphy has been deemed the most appropriate in the research setting; however, technological advances have seen the emergence of automatic-scoring wearable devices and software. Methods  A total of 60-nights of sleep data from 20-healthy adult participants (10 male, 10 female, age: 26 ± 10 years) were collected while wearing two devices concomitantly. The objective was to compare an automatic-scoring device (Fatigue Science Readiband™ [AUTO]) and a manually-scored device (Micro Motionlogger® [MAN]) based on the Cole-Kripke method. Manual-scoring involved trained technicians scoring all 60-nights of sleep data. Sleep indices including total sleep time (TST), total time in bed (TIB), sleep onset latency (SOL), sleep efficiency (SE), wake after sleep onset (WASO), wake episodes per night (WE), sleep onset time (SOT) and wake time (WT) were assessed between the two devices using mean differences, 95% levels of agreement, Pearson-correlation coefficients ( r ), and typical error of measurement (TEM) analysis. Results  There were no significant differences between devices for any of the measured sleep variables ( p  ≥0.05). All sleep indices resulted in very-strong correlations ( all r  ≥0.84) between devices. A mean difference between devices of <1 minutes for TST was associated with a TEM of 15.5 minute (95% CI =12.3 to 17.7 minutes). Conclusion  Given there were no significant differences between devices in the current study, automatic-scoring actigraphy devices may provide a more practical and cost-effective alternative to manually-scored actigraphy in healthy populations

    Diagnosing Schizophrenia from Activity Records using Hidden Markov Model Parameters

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    The diagnosis of Schizophrenia is mainly based on qualitative characteristics. With the usage of portable devices which measure activity of humans, the diagnosis of Schizophrenia can be enriched through quantitative features. The goal of this work is to classify between schizophrenic and non-schizophrenic subjects based on their measured activity over a certain amount of time. To do so, the periods in which a subject was resting or active were identified by the application of a Hidden Markov model (HMM). The trained model parameters of the HMM, such as the mean or variance of activity during the state of rest or activity, are used as classification features for a logistic regression model. Our results indicate that the features from the HMM are significant in classifying between schizophrenic and non-schizophrenic subjects. Moreover, the features outperform the features derived through other methods in literature in terms of goodness-of-fit and classification performance.acceptedVersio

    Identifying Bedrest Using Waist-worn Triaxial Accelerometers in Preschool Children

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    Purpose To adapt and validate a previously developed decision tree for youth to identify bedrest for use in preschool children. Methods Parents of healthy preschool (3-6-year-old) children (n = 610; 294 males) were asked to help them to wear an accelerometer for 7 to 10 days and 24 hours/day on their waist. Children with ≥3 nights of valid recordings were randomly allocated to the development (n = 200) and validation (n = 200) groups. Wear periods from accelerometer recordings were identified minute-by-minute as bedrest or wake using visual identification by two independent raters. To automate visual identification, chosen decision tree (DT) parameters (block length, threshold, bedrest-start trigger, and bedrest-end trigger) were optimized in the development group using a Nelder-Mead simplex optimization method, which maximized the accuracy of DT-identified bedrest in 1-min epochs against synchronized visually identified bedrest (n = 4,730,734). DT\u27s performance with optimized parameters was compared with the visual identification, commonly used Sadeh’s sleep detection algorithm, DT for youth (10-18-years-old), and parental survey of sleep duration in the validation group. Results On average, children wore an accelerometer for 8.3 days and 20.8 hours/day. Comparing the DT-identified bedrest with visual identification in the validation group yielded sensitivity = 0.941, specificity = 0.974, and accuracy = 0.956. The optimal block length was 36 min, the threshold 230 counts/min, the bedrest-start trigger 305 counts/min, and the bedrest-end trigger 1,129 counts/min. In the validation group, DT identified bedrest with greater accuracy than Sadeh’s algorithm (0.956 and 0.902) and DT for youth (0.956 and 0.861) (both P\u3c0.001). Both DT (564±77 min/day) and Sadeh’s algorithm (604±80 min/day) identified significantly less bedrest/sleep than parental survey (650±81 min/day) (both P\u3c0.001). Conclusions The DT-based algorithm initially developed for youth was adapted for preschool children to identify time spent in bedrest with high accuracy. The DT is available as a package for the R open-source software environment (“PhysActBedRest”)

    Vauvojen unen luokittelu patja-sensorilla ja EKG:lla

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    Infants spend the majority of their time asleep. Although extensive studies have been carried out, the role of sleep for infant cognitive, psychomotor, temperament and developmental outcomes is not clear. The current contradictory results may be due to the limited precision when monitoring infant sleep for prolonged periods of time, from weeks to even months. Sleep-wake cycle can be assessed with sleep questionnaires and actigraphy, but they cannot separate sleep stages. The gold standard for sleep state annotation is polysomnography (PSG), which consist of several signal modalities such as electroencephalogram, electrooculogram, electrocardiogram (ECG), electromyogram, respiration sensor and pulse oximetry. A sleep clinician manually assigns sleep stages for 30 sec epochs based on the visual observation of these signals. Because method is obtrusive and laborious it is not suitable for monitoring long periods. There is, therefore, a need for an automatic and unobtrusive sleep staging approach. In this work, a set of classifiers for infant sleep staging was created and evaluated. The cardiorespiratory and gross body movement signals were used as an input. The different classifiers aim to distinguish between two or more different sleep states. The classifiers were built on a clinical sleep polysomnography data set of 48 infants with ages ranging from 1 week to 18 weeks old (a median of 5 weeks). Respiration and gross body movements were observed using an electromechanical film bed mattress sensor manufactured by Emfit Ltd. ECG of the PSG setup was used for extracting cardiac activity. Signals were preprocessed to remove artefacts and an extensive set of features (N=81) were extracted on which the classifiers were trained. The NREM3 vs other states classifier provided the most accurate results. The median accuracy was 0.822 (IQR: 0.724-0.914). This is comparable to previously published studies on other sleep classifiers, as well as to the level of clinical interrater agreement. Classification methods were confounded by the lack of muscle atonia and amount of gross body movements in REM sleep. The proposed method could be readily applied for home monitoring, as well as for monitoring in neonatal intensive care units.Vauvat nukkuvat suurimman osan vuorokaudesta. Vaikkakin laajasti on tutkittu unen vaikutusta lapsen kognitioon, psykomotoriikkaan, temperamenttiin ja kehitykseen, selkeää kuvaa ja yhtenäistä konsensusta tiedeyhteisössä ei ole saavutettu. Yksi syy tähän on että ei ole olemassa menetelmää, joka soveltuisi jatkuva-aikaiseen ja pitkäkestoiseen unitilan monitorointiin. Vauvojen uni-valve- sykliä voidaan selvittää vanhemmille suunnatuilla kyselyillä ja aktigrafialla, mutta näillä ei voi havaita unitilojen rakennetta. Kliinisenä standardina unitilojen seurannassa on polysomnografia, jossa samanaikaisesti mitataan mm. potilaan elektroenkelografiaa, elektro-okulografiaa, elektrokardiografiaa, electromyografiaa, hengitysinduktiivisesta pletysmografiaa, happisaturaatiota ja hengitysvirtauksia. Kliinikko suorittaa univaiheluokittelun signaaleista näkyvien, vaiheille tyypillisten, hahmojen perusteella. Työläyden ja häiritsevän mittausasetelman takia menetelmä ei sovellu pitkäaikaiseen seurantaan. On tarvetta kehittää tarkoitukseen sopivia automaattisia ja huomaamattomia unenseurantamenetelmiä. Tässä työssä kehitettiin ja testattiin sydämen syke-, hengitys ja liikeanalyysiin perustuvia koneluokittimia vauvojen unitilojen havainnointiin. Luokittimet opetettiin kliinisessa polysomnografiassa kerätyllä datalla 48 vauvasta, joiden ikä vaihteli 1. viikosta 18. viikkoon (mediaani 5 viikkoa). Vauvojen hengitystä ja liikkeitä seurattiin Emfit Oy:n valmistamalla elektromekaaniseen filmiin pohjatuvalla patja-sensorilla. Lisäksi ECG:lla seurattiin sydäntä ja opetuksessa käytettiin lääkärin suorittamaa PSG-pohjaista luokitusta. Esikäsittelyn jälkeen signaaleista laskettiin suuri joukko piirrevektoreita (N=81), joihin luokittelu perustuu. NREM3-univaiheen tunnistus onnistui parhaiten 0.822 mediaani-tarkkuudella ja [0.724,0.914] kvartaaleilla. Tulos on yhtenevä kirjallisuudessa esitettyjen arvojen kanssa ja vastaa kliinikkojen välistä toistettavuutta. Muilla luokittimilla univaiheet sekoituivat keskenään, mikä on oletattavasti selitettävissä aikuisista poikeavalla REM-unen aikaisella lihasjäykkyydellä ja kehon liikkeillä. Työ osoittaa, että menetelmällä voi seurata vauvojen uniluokkien oskillaatiota. Järjestelmää voisi käyttää kotiseurannassa tai vastasyntyneiden teholla unenvalvontaan
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