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Gaussian model for movement detection during Sleep

A. M. Adami, A. G. Adami, T. L. Hayes, M. Pavel, Z. T. Beattie
2012 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
We use a single univariate Gaussian model to represent each class: movement versus non-movement.  ...  This paper describes a method for detection of movement in bed that is evaluated on data collected from patients admitted for regular polysomnography.  ...  In this paper, we have extended our previous work on movement detection and describe a method for detection that uses a Gaussian model to discriminate movement and non-movement and that is independent  ... 
doi:10.1109/embc.2012.6346413 pmid:23366374 pmcid:PMC3563107 fatcat:i6oxygczsvbdhpzcghvlwdkwry

Sleep spindles detection from human sleep EEG signals using autoregressive (AR) model: a surrogate data approach

Venkatakrishnan Perumalsamy, Sangeetha Sankaranarayanan, Sukanesh Rajamony
2009 Journal of Biomedical Science and Engineering  
A new algorithm for the detection of sleep spindles from human sleep EEG with surrogate data approach is presented.  ...  The algorithm work well for the detection of sleep spindles and in addition the analysis reveals the alpha and beta band activities in EEG.  ...  Kristina Susmakova for providing EEG sleep data and invaluable help and discussions.  ... 
doi:10.4236/jbise.2009.25044 fatcat:6kqmma6vo5b2dpaemy7bkxayxi

An actigraphy heterogeneous mixture model for sleep assessment

A. Domingues, T. Paiva, J. M. Sanches
2012 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
Thus it is possible to refine the discrimination level when detecting these states.  ...  The proposed methodology to characterize the actigraphy data is based on a mixture of three canonical distributions; i)Exponential, ii)Rayleigh and iii)Gaussian.  ...  Fig . 1. a) Actigraphy data and detected movements (top) b) and Hypnogram (bottom). Fig. 2 . 2 Histogram of movements during sleep and wake states.  ... 
doi:10.1109/embc.2012.6346416 pmid:23366377 dblp:conf/embc/DominguesPS12 fatcat:67234bzqjfaxfavcyjjx76brvm

Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis

Yuan-Kai Wang, Hong-Yu Chen, Jian-Ru Chen
2019 Electronics  
This paper proposes a robust method for sleep pose analysis with human joints model.  ...  The challenge of video surveillance for sleep behavior analysis is that we have to tackle bad image illumination issue and large pose variations during sleeping.  ...  The authors gratefully acknowledge the support of Chia-Mo Lin and Hou-Chang Chiu for their valuable comments in obstructive sleep apnea.  ... 
doi:10.3390/electronics8070812 fatcat:ditgc4n5ordxzpskqrhppuso6q

Modelling of Behavioural Patterns for Abnormality Detection in the Context of Lifestyle Reassurance [chapter]

Fabien Cardinaux, Simon Brownsell, Mark Hawley, David Bradley
2008 Lecture Notes in Computer Science  
This paper proposes a new approach for detection of individual deviation from normal behaviour focusing on building probabilistic models of behaviour based on a set of activity attributes.  ...  Models are trained using only normal behaviour. Variations from the models are considered as abnormal behaviours and these can be highlighted for subsequent review or intervention.  ...  Acknowledgement We are grateful to project participants and Department of Health (HTD313) for funding this work.  ... 
doi:10.1007/978-3-540-85920-8_30 fatcat:72r6wlfzcbhk3cwvvnvuhubyqy

Snore Sound Analysis Can Detect the Presence of Obstructive Sleep Apnea Specific to NREM or REM Sleep

Shahin Akhter, Udantha R. Abeyratne, Vinayak Swarnkar, Craig Hukins
2018 Journal of Clinical Sleep Medicine (JCSM)  
Results: The models achieved 80% to 86% accuracy for detecting OSA in NREM sleep and 82% to 85% in REM sleep.  ...  Study Objectives: Severities of obstructive sleep apnea (OSA) estimated both for the overall sleep duration and for the time spent in rapid eye movement (REM) and non-rapid eye movement (NREM) sleep are  ...  Brett Duce, Scientific Director, Sleep Disorders Centre, Department of Respiratory and Sleep Medicine, Princess Alexandra Hospital, Brisbane, Australia, for the valuable assistance with clinical data acquisition  ... 
doi:10.5664/jcsm.7168 pmid:29852905 pmcid:PMC5991962 fatcat:unfbobsjzzcszi5z465q5o5yju

SleepGuardian: An RF-based Healthcare System Guarding Your Sleep from Afar [article]

Yu Gu and Yantong Wang and Zhi Liu and Jun Liu and Jie Li
2019 arXiv   pre-print
The online service keeps guarding the subject for any abnormal behaviors during sleep like intensive body twitches and a sudden seizure attack.  ...  crucial for their wellness.  ...  ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers for their time and effort.  ... 
arXiv:1908.06171v1 fatcat:gdjba2blxjd53hd2uxhtadn5ca

Cough detection using a non-contact microphone: A nocturnal cough study

Marina Eni, Valeria Mordoh, Yaniv Zigel, Felix Albu
2022 PLoS ONE  
Two different classifiers were implemented and tested: a Gaussian Mixture Model (GMM) and a Deep Neural Network (DNN).  ...  Cough events were significantly more frequent during wakefulness than in the sleep stages (p < 0.0001) and were significantly less frequent during deep sleep than in other sleep stages (p < 0.0001).  ...  Ariel Tarasiuk and Bruria Friedman, from the Sleep-Wake Disorder Unit, Soroka University Medical Center for their clinical data acquisition support.  ... 
doi:10.1371/journal.pone.0262240 pmid:35045111 pmcid:PMC8769326 fatcat:gsjpyi62o5hr7g7jmfzzp5pkeq

Contactless Interactive Fall Detection and Sleep Quality Estimation for Supporting Elderly with Incipient Dementia

Roman Siedel, Tobias Scheck, Ana C. Perez Grassi, Julian B. Seuffert, André Apitzsch, Jingrui Yu, Norbert Nestler, Danny Heinz, Lars Lehmann, Anne Goy, Gangolf Hirtz
2020 Current Directions in Biomedical Engineering  
Furthermore, sleep quality estimation is employed to be able to draw conclusions about the current behaviour of an affected person. This article presents the current development state of AUXILIA.  ...  AUXILIA is such an AAL system and does not only support elderly people with dementia in an early phase, but also monitors their activities to provide behaviour analysis results for care attendants, relatives  ...  A well known and in the past often used indicator to determine sleep quality is the movement during sleep using images [11, 12] .  ... 
doi:10.1515/cdbme-2020-3100 fatcat:uwrb6z7tdzhuvjzmjmnacrvd3u

A model-based detector of vertex waves and K complexes in sleep electroencephalogram

A.C. Da Rosa, B. Kemp, T. Paiva, F.H. Lopes da Silva, H.A.C. Kamphuisen
1991 Electroencephalography and Clinical Neurophysiology  
A model-based method for the detection of sleep phasic events was implemented in a personal computer. Its performance was investigated using simulated and real whole-night EEG signals.  ...  A model of sleep phasic events such as vertex waves, K complexes, delta waves and sleep spindles is proposed.  ...  Nunes LeitSo for his helpful discussions and orientations given to this work, and Dr. A.C. Declerck and Prof. P. Halasz for the introduction to the problem of K complexes.  ... 
doi:10.1016/0013-4694(91)90021-u pmid:1701718 fatcat:57se6525y5antowyqylaz3wksm

Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition

Bingni W. Brunton, Lise A. Johnson, Jeffrey G. Ojemann, J. Nathan Kutz
2016 Journal of Neuroscience Methods  
Next, we leveraged DMD in combination with machine learning to develop a novel method to extract sleep spindle networks from the same subjects.  ...  Here we report the adaptation of dynamic mode decomposition (DMD), an algorithm originally developed for the study of fluid physics, to large-scale neuronal recordings.  ...  Blakely for collecting the dataset used for the sensorimotor mapping. Funding.  ... 
doi:10.1016/j.jneumeth.2015.10.010 pmid:26529367 fatcat:yumbp2btprge7bn7dpvag4z3sq

Estimating actigraphy from motion artifacts in ECG and respiratory effort signals

Pedro Fonseca, Ronald M Aarts, Xi Long, Jérôme Rolink, Steffen Leonhardt
2015 Physiological Measurement  
The potential of using this estimation as a method to detect body movements and as a replacement of wrist-actigraphy for sleep/wake classification is evaluated.  ...  These results suggest that surrogate actigraphy is adequate for quantifying small body movements and, overall, to detect the presence of movements of any intensity and henceforth, to classify sleep and  ... 
doi:10.1088/0967-3334/37/1/67 pmid:26641863 fatcat:zungx7f7pnf2bjgbhh42re4uum

Wireless Wearable Multisensory Suite and Real-Time Prediction of Obstructive Sleep Apnea Episodes

T. Q. Le, Changqing Cheng, A. Sangasoongsong, W. Wongdhamma, S. T. S. Bukkapatnam
2013 IEEE Journal of Translational Engineering in Health and Medicine  
delivery during their sleep.  ...  INDEX TERMS Sleep apnea, Gaussian mixture model, Nonlinear dynamical systems, Biomedical telemetry. Trung Q. Le (M'08) was born in Da Nang, Vietnam.  ...  For the first experimental epoch (sleep through one night), each subject was requested to use only the sleep device (from Zeo) during sleep.  ... 
doi:10.1109/jtehm.2013.2273354 pmid:27170854 pmcid:PMC4819230 fatcat:mipiapmpwrfo3a5wlrxvw5ri7u

A model-based monitor of human sleep stages

B. Kemp, E. W. Gr�neveld, A. J. M. W. Janssen, J. M. Franzen
1987 Biological cybernetics  
Stochastic models are proposed for sleep and for the sleep related electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG).  ...  The EOG and EMG are modelled as combinations of Poisson point processes and Gaussian processes, respectively. The EEG models contain a feedback structure that is based on physiological data.  ...  Eye Movement Observations A variety of EOG preprocessors for the detection of REM-bursts and SEMs have been described (e.g.  ... 
doi:10.1007/bf00354982 pmid:3435725 fatcat:fgz5y43ckjglnaiv272bkffo7e

A Hidden Markov Model Based Unsupervised Algorithm for Sleep/Wake Identification Using Actigraphy [article]

Xinyue Li, Yunting Zhang, Fan Jiang, Hongyu Zhao
2020 arXiv   pre-print
Positive predictive values for sleep epochs were 85.6% and 84.6% for HMM and AS, respectively, and 95.5% and 85.6% for wake epochs.  ...  Actigraphy is widely used in sleep studies but lacks a universal unsupervised algorithm for sleep/wake identification.  ...  rare movements during sleep.  ... 
arXiv:1812.00553v2 fatcat:ifcv6saqcjasznyopfj7do3tzy
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