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Sleep

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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
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
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
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...

Standardized image-based polysomnography database and deep learning algorithm for sleep-stage classification.

Sleep
STUDY OBJECTIVES: Polysomnography (PSG) scoring is labor-intensive, subjective, and often ambiguous. Recently several deep learning (DL) models for automated sleep scoring have been developed, they are tied to a fixed amount of input channels and res...

Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
UNLABELLED: Since the publication of its 2020 position statement on artificial intelligence (AI) in sleep medicine by the American Academy of Sleep Medicine, there has been a tremendous expansion of AI-related software and hardware options for sleep ...

A convolutional neural network-based decision support system for neonatal quiet sleep detection.

Mathematical biosciences and engineering : MBE
Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to det...

SleepSIM: Conditional GAN-based non-REM sleep EEG Signal Generator.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Synthetic data generation has become increasingly popular with the increasing use of generative networks. Recently, Generative Adversarial Network (GAN) architectures have produced exceptional results in synthetic image generation. However, time seri...

Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring.

Sleep
STUDY OBJECTIVES: To quantify the amount of sleep stage ambiguity across expert scorers and to validate a new auto-scoring platform against sleep staging performed by multiple scorers.

A Developed LSTM-Ladder-Network-Based Model for Sleep Stage Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sleep staging is crucial for diagnosing sleep-related disorders. The heavy and time-consuming task of manual staging can be released by automatic techniques. However, the automatic staging model would have a relatively poor performance when working o...