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Human activity can be measured with actimetry sensors used by the subjects in several locations such as the wrists or legs. Actigraphy data is used in different contexts such as sports training or tele-medicine monitoring. In the diagnosis of sleep disorders, the actimetry sensor, which is basically a 3D axis accelerometer, is used by the patient in the non dominant wrist typically during an entire...
As part of a sleep monitoring project, we used actigraphy based on body-worn accelerometer sensors to remotely monitor and study the sleep-wake cycle of elderly staying at nursing homes. We have conducted a fifteen patient trial of a sleep activity pattern monitoring (SAPM) system at a local nursing home. The data was collected and stored in our server and the processing of the data was done offline...
To evaluate sleep quality or autonomic nervous system, many annoying electrodes have be attached to subjects' body. It can disturb comfortable sleep and, moreover, since it is very expensive experiment, continuous sleep monitoring is difficult. Since heart rate reflects the autonomic nervous system, it is highly synchronized with the sympathetic activation during transition from non-REM sleep to wakefulness...
We describe a contact-less method for measurement of respiration rate during sleep using a 5.8GHz radio-frequency bio-motion sensor. The sensor operates by sensing phase shifts in reflected radio waves from the torso caused by respiratory movements and other bodily movements such as twitches, positional changes etc. These non-respiratory motion artefacts can obscure reliable estimation of breathinig...
This study aims to investigate the measurement performance on different sensor deployment and to determine the optimal position for monitoring heart rate (HR) and respiration rate (RR) during sleep. Five identical sensor boards were deployed on different positions simultaneously during sleep to detect changes of applied pressure due to heart beating and breathing. One board was set beneath the pillow;...
Sleep science and respiratory engineering as medical subspecialties and research areas grew up side-by-side with biomedical engineering. The formation of EMBS in the 1950's and the discovery of REM sleep in the 1950's led to parallel development and interaction of sleep and biomedical engineering in diagnostics and therapeutics.
A hydraulic bed sensor has been developed to non-invasively monitor pulse and respiration during sleep. This sensor is designed for in-home use, to be part of an integrated sensor network for the early detection of illness and functional decline in elderly adults. Experience with another bed sensor has motivated a desire to acquire enhanced, quantitative data related to pulse and respiration. This...
Disrupted sleep patterns are a significant problem in the elderly, leading to increased cognitive dysfunction and risk of nursing home placement. A cost-effective and unobtrusive way to remotely monitor changing sleep patterns over time would enable improved management of this important health problem. We have developed an algorithm to derive sleep parameters such as bed time, rise time, sleep latency,...
In the present work we apply a fully automatic procedure to the analysis of signal coming from a sensorized T-shit, worn during the night, for sleep evaluation. The goodness and reliability of the signals recorded trough the T-shirt was previously tested, while the employed algorithms for feature extraction and sleep classification were previously developed on standard ECG recordings and the obtained...
An automated real time method for detecting human breathing rate from a non contact biosensor is considered in this paper. The method has low computational and RAM requirements making it well-suited to real-time, low power implementation on a microcontroller. Time and frequency domain methods are used to separate a 15s block of data into movement, breathing or absent states; a breathing rate estimate...
A bed was equipped with four force transducers so that the location of the Center Of Mass (COM) can be computed, when the bed contains a person. The computation of the COM and its alterations in combination with the sum of all measured forces allows to compute the person's position in bed, an activity level, the resulting body weight and the corresponding weight change rate over time (overnight and...
In this manuscript we present an overview of novel signal processing techniques developed by our group to reduce scoring time in the assessment of the severity of sleep-related breathing disorders in heart failure patients and to detect sleep/wake fluctuations during periodic breathing. Besides describing these methods, we present the results of validation experiments. Our work shows that novel signal...
A patient's sleep/wake schedule is an important step underlying clinical evaluation of sleep-related complaints. Aspects related to timing of a person's sleep routine provide important clues regarding diagnosis and treatments. Solutions for sleep complaints may sometimes rely solely on changes in habits and life style, based on what is learned from daily rest-activity patterns. This paper describes...
Automatic detection of the sleep macrostructure (Wake, NREM -non Rapid Eye Movement- and REM-Rapid Eye Movement-) based on bed sensor signals is presented. This study assesses the feasibility of different methodologies to evaluate the sleep quality out of sleep centers. The study compares a) the features extracted from time-variant autoregressive modeling (TVAM) and Wavelet Decomposition (WD) and...
Currently Long QT Syndrome (LQTS) is diagnosed by using the Long QT Syndrome “diagnostic score”. Calculation of the score is done by assigning different points to various criteria. The answers to the criteria are often hard to obtain as they require lengthy periods of cardiac observations. And even after the scores are obtain, only a percentage of certainty is obtained. Diagnosis of LQTS is often...
Summary form only given. An overview is presented of different studies on monitoring and detection of drowsiness and microsleep (MS)during driving simulation. At first a framework is presented how to utilize methods of pattern recognition and of computational intelligence in order to detect and to predict MS. Secondly, different biosignals are compared due to their value for MS detection. Data fusion...
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