AIMC Topic: Polysomnography
Clear Filters Showing 161 to 170 of 242 articles
Medical Mechatronics for Healthcare.
Journal of healthcare engineering
Apr 18, 2018
Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques.
Biomedical engineering online
Feb 1, 2018
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 ...
A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Jan 31, 2018
OBJECTIVE: Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels have long been recognized. Manual staging is resour...
Performance comparison between wrist and chest actigraphy in combination with heart rate variability for sleep classification.
Computers in biology and medicine
Aug 8, 2017
The concurrent usage of actigraphy and heart rate variability (HRV) for sleep efficiency quantification is still matter of investigation. This study compared chest (CACT) and wrist (WACT) actigraphy (actigraphs positioned on chest and wrist, respecti...
Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomnography Resources?
Methods of information in medicine
Jun 7, 2017
OBJECTIVES: The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic va...
Correlates of sleep quality in midlife and beyond: a machine learning analysis.
Sleep medicine
Mar 27, 2017
OBJECTIVES: In older adults, traditional metrics derived from polysomnography (PSG) are not well correlated with subjective sleep quality. Little is known about whether the association between PSG and subjective sleep quality changes with age, or whe...
Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy.
Journal of neuroscience methods
Feb 20, 2017
BACKGROUND: Type 1 narcolepsy (NT1) is characterized by symptoms believed to represent Rapid Eye Movement (REM) sleep stage dissociations, occurrences where features of wake and REM sleep are intermingled, resulting in a mixed state. We hypothesized ...
Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis.
IEEE journal of biomedical and health informatics
Jan 9, 2017
Effective sleep analysis is hampered by the lack of automated tools catering to disordered sleep patterns and cumbersome monitoring hardware. In this paper, we apply deep learning on a set of 57 EEG features extracted from a maximum of two EEG channe...
Bayesian Nonnegative CP Decomposition-Based Feature Extraction Algorithm for Drowsiness Detection.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Oct 19, 2016
Daytime short nap involves physiological processes, such as alertness, drowsiness and sleep. The study of the relationship between drowsiness and nap based on physiological signals is a great way to have a better understanding of the periodical rhyme...
Sensitivity and Specificity
Male
Female
Data Interpretation, Statistical
Humans
Reproducibility of Results
Polysomnography
Diagnosis, Computer-Assisted
Bayes Theorem
Machine Learning
Young Adult
Models, Statistical
Sleep Stages
Electroencephalography
Algorithms
Adult
Pattern Recognition, Automated
Computer Simulation
Support vector machines for automated snoring detection: proof-of-concept.
Sleep & breathing = Schlaf & Atmung
Jul 13, 2016
BACKGROUND: Snoring has been shown to be associated with adverse physical and mental health, independent of the effects of sleep disordered breathing. Despite increasing evidence for the risks of snoring, few studies on sleep and health include objec...
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