AIMC Topic: Sleep

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Challenges of Applying Automated Polysomnography Scoring at Scale.

Sleep medicine clinics Jun 15, 2023
Automatic polysomnography analysis can be leveraged to shorten scoring times, reduce associated costs, and ultimately improve the overall diagnosis of sleep disorders. Multiple and diverse strategies have been attempted for implementation of this tec...
Humans Sleep Stages Polysomnography Sleep Wake Disorders Artificial Intelligence Sleep
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TransSleep: Transitioning-Aware Attention-Based Deep Neural Network for Sleep Staging.

IEEE transactions on cybernetics Jun 15, 2023
Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine/deep learning methods for sleep staging. However, two key challenges hinder the practical use of those meth...
Neural Networks, Computer Sleep Electroencephalography Sleep Stages Machine Learning
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Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

Journal of medical Internet research Jun 1, 2023
BACKGROUND: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via d...
Home Environment Humans Deep Learning Sound Recordings Sleep Smartphone Sleep Stages
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Performance of a Convolutional Neural Network Derived From PPG Signal in Classifying Sleep Stages.

IEEE transactions on bio-medical engineering May 19, 2023
Automatic sleep stage classification is vital for evaluating the quality of sleep. Conventionally, sleep is monitored using multiple physiological sensors that are uncomfortable for long-term monitoring and require expert intervention. In this study,...
Neural Networks, Computer Polysomnography Sleep Stages Electroencephalography Sleep
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AI-Driven sleep staging from actigraphy and heart rate.

PloS one May 17, 2023
Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). H...
Sleep Stages Time Factors Reproducibility of Results Humans Sleep Heart Rate Male Artificial Intelligence Actigraphy
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Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea.

Sensors (Basel, Switzerland) May 12, 2023
An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnography is recommended by health experts to detect obstructive sleep apnea. The patient is paired up wit...
Sleep Humans Deep Learning Sleep Apnea, Obstructive Algorithms Reproducibility of Results
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MaskSleepNet: A Cross-Modality Adaptation Neural Network for Heterogeneous Signals Processing in Sleep Staging.

IEEE journal of biomedical and health informatics May 4, 2023
Deep learning methods have become an important tool for automatic sleep staging in recent years. However, most of the existing deep learning-based approaches are sharply constrained by the input modalities, where any insertion, substitution, and dele...
Sleep Stages Sleep Polysomnography Humans Neural Networks, Computer Electroencephalography
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A Comparison of Signal Combinations for Deep Learning-Based Simultaneous Sleep Staging and Respiratory Event Detection.

IEEE transactions on bio-medical engineering Apr 20, 2023
OBJECTIVE: Obstructive sleep apnea (OSA) is diagnosed using the apnea-hypopnea index (AHI), which is the average number of respiratory events per hour of sleep. Recently, machine learning algorithms for automatic AHI assessment have been developed, b...
Sleep Apnea, Obstructive Sleep Sleep Stages Humans Polysomnography Deep Learning
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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.
Aged Deep Learning Child Sleep Polysomnography Humans Sleep Stages Neural Networks, Computer
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Coupling analysis of heart rate variability and cortical arousal using a deep learning algorithm.

PloS one Apr 6, 2023
Frequent cortical arousal is associated with cardiovascular dysfunction among people with sleep-disordered breathing. Changes in heart rate variability (HRV) can represent pathological conditions associated with autonomic nervous system dysfunction. ...
Algorithms Female Heart Rate Deep Learning Sleep Humans Arousal Electroencephalography Autonomic Nervous System
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