AIMC Topic: Polysomnography

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Automatic sleep staging for the young and the old - Evaluating age bias in deep learning.

Sleep medicine Apr 13, 2023
BACKGROUND: Various deep-learning systems have been proposed for automated sleep staging. Still, the significance of age-specific underrepresentation in training data and the resulting errors in clinically used sleep metrics are unknown.
Neural Networks, Computer Aged Humans Sleep Polysomnography Sleep Stages Deep Learning Child
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Pediatric Automatic Sleep Staging: A Comparative Study of State-of-the-Art Deep Learning Methods.

IEEE transactions on bio-medical engineering Nov 21, 2022
BACKGROUND: Despite the tremendous prog- ress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overn...
Sleep Stages Deep Learning Humans Sleep Sleep Apnea, Obstructive Child Polysomnography Adult
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Detection of K-complexes in EEG waveform images using faster R-CNN and deep transfer learning.

BMC medical informatics and decision making Nov 17, 2022
BACKGROUND: The electroencephalography (EEG) signal carries important information about the electrical activity of the brain, which may reveal many pathologies. This information is carried in certain waveforms and events, one of which is the K-comple...
Neural Networks, Computer Brain Deep Learning Humans Electroencephalography Polysomnography
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An Improved Neural Network Based on SENet for Sleep Stage Classification.

IEEE journal of biomedical and health informatics Oct 4, 2022
Sleep staging is an important step in analyzing sleep quality. Traditional manual analysis by psychologists is time-consuming. In this paper, we propose an automatic sleep staging model with an improved attention module and hidden Markov model (HMM)....
Sleep Polysomnography Neural Networks, Computer Electroencephalography Sleep Stages Humans
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Sleep classification using Consumer Sleep Technologies and AI: A review of the current landscape.

Sleep medicine Sep 22, 2022
Classifying sleep stages in real-time represents considerable potential, for instance in enabling interactive noise masking in noisy environments when persons are in a state of light sleep or to support clinical staff in analyzing sleep patterns etc....
Sleep Polysomnography Sleep Stages Artificial Intelligence Algorithms Humans
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A systematic review of deep learning methods for modeling electrocardiograms during sleep.

Physiological measurement Aug 12, 2022
Sleep is one of the most important human physiological activities, and plays an essential role in human health. Polysomnography (PSG) is the gold standard for measuring sleep quality and disorders, but it is time-consuming, labor-intensive, and prone...
Polysomnography Humans Deep Learning Sleep Apnea Syndromes Electrocardiography Sleep
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A multi-modal assessment of sleep stages using adaptive Fourier decomposition and machine learning.

Computers in biology and medicine Jul 14, 2022
Healthy sleep is essential for the rejuvenation of the body and helps in maintaining good health. Many people suffer from sleep disorders that are characterized by abnormal sleep patterns. Automated assessment of such disorders using biomedical signa...
Polysomnography Sleep Stages Electroencephalography Humans Electrooculography Signal Processing, Computer-Assisted Machine Learning
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Accurate contactless sleep apnea detection framework with signal processing and machine learning methods.

Methods (San Diego, Calif.) Jul 1, 2022
The detection of sleep apnea is critical for assessing sleep quality. It is also a proven biometric in diagnosing cardiovascular and other diseases. Recent studies have shown that radar-based non-contact vital sign monitoring system can effectively d...
Humans Sleep Apnea Syndromes Polysomnography Machine Learning Signal Processing, Computer-Assisted
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Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans.

Sleep medicine Apr 29, 2022
BACKGROUND: Obstructive sleep apnea (OSA) remains massively underdiagnosed, due to limited access to polysomnography (PSG), the highly complex gold standard for diagnosis. Performance scores in predicting OSA are evaluated for machine learning (ML) a...
Head Skull Middle Aged Cephalometry Aged Humans Sleep Apnea, Obstructive Surveys and Questionnaires Male Imaging, Three-Dimensional Machine Learning Polysomnography Mass Screening
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Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea.

Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale Apr 25, 2022
BACKGROUND: Obstructive sleep apnea is a common clinical condition and has a significant impact on the health of patients if untreated. The current diagnostic gold standard for obstructive sleep apnea is polysomnography, which is labor intensive, req...
Artificial Intelligence Humans Sleep Apnea, Obstructive Polysomnography
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