AIMC Topic: Sleep
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Supervised machine learning on electrocardiography features to classify sleep in noncritically ill children.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Feb 1, 2025
STUDY OBJECTIVES: Despite frequent sleep disruption in the pediatric intensive care unit, bedside sleep monitoring in real time is currently not available. Supervised machine learning applied to electrocardiography data may provide a solution, becaus...
What radio waves tell us about sleep!
Sleep
Jan 13, 2025
The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow...
MS Pattern Explorer: interactive visual exploration of temporal activity patterns for multiple sclerosis.
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2024
OBJECTIVES: This article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables' data. Applied to a clinical study of multiple sclerosis (MS) patients, the t...
STConvSleepNet: A Spatiotemporal Convolutional Network for Sleep Posture Detection.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Sleep posture, intricately connected to sleep health, has emerged as a crucial focus in sleep medicine. Studies have associated the supine posture with increased frequency and severity of obstructive sleep apnea (OSA), while lateral postures may miti...
Predicting Sleep Quality via Unsupervised Learning of Cardiac Activity.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
While highly important for a person's mood, productivity, and physical performance, perceived sleep quality is challenging to model and, thus, predict with passive means such as physiological and behavioral signals alone. In this paper, we propose a ...
Machine Learning Model Combining Ventilatory, Hypoxic, Arousal Domains Across Sleep Better Predicts Adverse Consequences of Obstructive Sleep Apnea.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Obstructive sleep apnea(OSA) severity is currently assessed clinically using the apnea-hypopnea index (AHI), which is inconsistently associated with short- and long-term outcomes. Ventilatory, hypoxic, and arousal domains are known to exhibit abnorma...
Laying the Foundation: Modern Transformers for Gold-Standard Sleep Analysis and Beyond.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Accurate sleep assessment is critical to the practice of sleep medicine and sleep research. The recent availability of large quantities of publicly available sleep data, alongside recent breakthroughs in AI like transformer architectures, present nov...
Ensemble Learning Approaches for Automatic Detection of Chronic Kidney Disease Stages during Sleep.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
This study investigates the use of ensemble learning methods for the automatic detection of chronic kidney disease (CKD) stages during sleep. We applied and evaluated four ensemble learning approaches-CatBoost, random forest, XGBoost, and LightGBM-to...
An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Mar 1, 2024
STUDY OBJECTIVES: Although sleep is frequently disrupted in the pediatric intensive care unit, it is currently not possible to perform real-time sleep monitoring at the bedside. In this study, spectral band powers of electroencephalography data are u...
Geriatric depression and anxiety screening via deep learning using activity tracking and sleep data.
International journal of geriatric psychiatry
Feb 1, 2024
BACKGROUND: Geriatric depression and anxiety have been identified as mood disorders commonly associated with the onset of dementia. Currently, the diagnosis of geriatric depression and anxiety relies on self-reported assessments for primary screening...
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