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Sleep

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EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis.

Computational and mathematical methods in medicine
Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of trad...

Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks.

IEEE journal of biomedical and health informatics
Unobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, whic...

Quiet sleep detection in preterm infants using deep convolutional neural networks.

Journal of neural engineering
OBJECTIVE: Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analy...

Sleep-wake classification via quantifying heart rate variability by convolutional neural network.

Physiological measurement
OBJECTIVE: Fluctuations in heart rate are intimately related to changes in the physiological state of the organism. We exploit this relationship by classifying a human participant's wake/sleep status using his instantaneous heart rate (IHR) series.

Real-time apnea-hypopnea event detection during sleep by convolutional neural networks.

Computers in biology and medicine
Sleep apnea-hypopnea event detection has been widely studied using various biosignals and algorithms. However, most minute-by-minute analysis techniques have difficulty detecting accurate event start/end positions. Furthermore, they require hand-engi...

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.

Scientific reports
Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanc...

ProvCaRe Semantic Provenance Knowledgebase: Evaluating Scientific Reproducibility of Research Studies.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Scientific reproducibility is critical for biomedical research as it enables us to advance science by building on previous results, helps ensure the success of increasingly expensive drug trials, and allows funding agencies to make informed decisions...

Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques.

Biomedical engineering online
PURPOSE: Breathing sounds during sleep are altered and characterized by various acoustic specificities in patients with sleep disordered breathing (SDB). This study aimed to identify acoustic biomarkers indicative of the severity of SDB by analyzing ...

Effect of a robotic seal on the motor activity and sleep patterns of older people with dementia, as measured by wearable technology: A cluster-randomised controlled trial.

Maturitas
OBJECTIVES: The robotic seal, PARO, has been used as an alternative to animal-assisted therapies with residents with dementia in long-term care, yet understanding of its efficacy is limited by a paucity of research. We explored the effects of PARO on...

Sleep in patients with disorders of consciousness characterized by means of machine learning.

PloS one
Sleep has been proposed to indicate preserved residual brain functioning in patients suffering from disorders of consciousness (DOC) after awakening from coma. However, a reliable characterization of sleep patterns in this clinical population continu...