AIMC Topic: Sleep Stages
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Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety.
Sensors (Basel, Switzerland)
Jul 17, 2023
Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has ...
Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea.
Sleep health
Jun 26, 2023
GOAL AND AIMS: Our objective was to evaluate the performance of Belun Ring with second-generation deep learning algorithms in obstructive sleep apnea (OSA) detection, OSA severity categorization, and sleep stage classification.
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...
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...
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...
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,...
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...
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...
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...
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.
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