Ergonomic sleep studies benefit from long-term monitoring in the home environment to cope with daily variations and habituation effects. Polysomnography allows to asses sleep accurately, but is costly, time-consuming and possibly disturbing for the sleeper. Actigraphy is cheap and user friendly, but for many studies lacks accuracy and detailed information. This proof-of-concept study investigates Least-Squares Support Vector Machines as a tool for automatic sleep stage classification (Wake-N1-Rem to N2-N3 separation), using automatic trainingset-specific filtered features as derived from three easy to register signals, namely heart rate, breathing rate and movement. The algorithms are trained and validated using 20 nights out of a 600 night database from over 100 different healthy persons. Different training and test set strategies were analyzed leading to different results. The more person-specific the training nights to the test nights, the better the classification accuracy as validated against the hypnograms scored by experts from the full polysomnograms. In the limit of complete person-specific training, the accuracy of the algorithm on the test set reached 94%. This means that this algorithm could serve its use in long-term monitoring sleep studies in the home environment, especially when prior person-specific polysomnographic training is performed.
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http://dx.doi.org/10.3233/WOR-2012-0419-1985 | DOI Listing |
Am J Rhinol Allergy
January 2025
Department of Radiology, Hangzhou First People's Hospital, Hangzhou, P. R. China.
Background: Computed tomography (CT) plays a crucial role in assessing chronic rhinosinusitis, but lacks objective quantifiable indicators.
Objective: This study aimed to use deep learning for automated sinus segmentation to generate distinct quantitative scores and explore their correlations with disease-specific quality of life.
Methods: From July 2021 to August 2022, 445 CT data were collected from 2 medical centers.
Exp Physiol
January 2025
Department for Automatics, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia.
The physiological sequelae of pre-term birth might influence the responses of this population to hypoxia. Moreover, identifying variables associated with development of acute mountain sickness (AMS) remains a key practically significant area of altitude research. We investigated the effects of pre-term birth on nocturnal oxygen saturation ( ) dynamics and assessed the predictive potential of nocturnal -related metrics for morning AMS in 12 healthy adults with gestational age < 32 weeks (pre-term) and 12 term-born control participants.
View Article and Find Full Text PDFJ Clin Endocrinol Metab
January 2025
Division of Orthogenetics, Department of Pediatrics, Nemours Children's Hospital, Delaware, 1600 Rockland Road, Wilmington, DE, 19803, USA.
Achondroplasia is the most common disproportionate short-stature skeletal dysplasia. Features associated with achondroplasia are rhizomelia, macrocephaly, midface hypoplasia, and typical cognition. Potential medical complications include foramen magnum stenosis, hydrocephalus, middle ear dysfunction, obstructive and central sleep apnea, spinal stenosis and genu varum.
View Article and Find Full Text PDFBrain Inform
January 2025
Department of Computing, Glasgow Caledonian University, Glasgow, G4 0BA, Scotland.
A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
The Medical Big Data Research Center, Northwest University, Xi'an, 710127 China.
Insomnia, as a common sleep disorder, is the most common complaints in medical practice affecting a large proportion of the population on a situational, recurrent or chronic basis. It has been demonstrated that, during wakefulness, patients with insomnia exhibit increased EEG power in theta, beta, and gamma band. However, the relevant mechanisms underlying such power changes are still lack of understanding.
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