AIMC Topic: Sleep Stages

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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...
Humans Deep Learning Sleep Stages Home Environment Sound Recordings Smartphone Sleep
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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...
Time Factors Heart Rate Reproducibility of Results Male Humans Sleep Sleep Stages Artificial Intelligence Actigraphy
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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...
Humans Sleep Stages Sleep Neural Networks, Computer Polysomnography 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...
Deep Learning Sleep Apnea, Obstructive Humans Sleep Stages Sleep 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.
Sleep Stages Deep Learning Child Sleep Polysomnography Neural Networks, Computer Aged Humans
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Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning.

Sensors (Basel, Switzerland) Mar 26, 2023
Sleep scoring involves the inspection of multimodal recordings of sleep data to detect potential sleep disorders. Given that symptoms of sleep disorders may be correlated with specific sleep stages, the diagnosis is typically supported by the simulta...
Sleep Stages Deep Learning Electroencephalography Sleep Sleep Wake Disorders Humans
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Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study.

Journal of medical Internet research Feb 10, 2023
BACKGROUND: Most existing automated sleep staging methods rely on multimodal data, and scoring a specific epoch requires not only the current epoch but also a sequence of consecutive epochs that precede and follow the epoch.
Sleep Stages Electroencephalography Humans Sleep Neural Networks, Computer Algorithms
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SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography.

IEEE journal of biomedical and health informatics Feb 3, 2023
Sleep staging is an essential component in the diagnosis of sleep disorders and management of sleep health. Sleep is traditionally measured in a clinical setting and requires a labor-intensive labeling process. We hypothesize that it is possible to p...
Humans Algorithms Sleep Stages Deep Learning Photoplethysmography Sleep
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A Flexible Deep Learning Architecture for Temporal Sleep Stage Classification Using Accelerometry and Photoplethysmography.

IEEE transactions on bio-medical engineering Dec 26, 2022
Wrist-worn consumer sleep technologies (CST) that contain accelerometers (ACC) and photoplethysmography (PPG) are increasingly common and hold great potential to function as out-of-clinic (OOC) sleep monitoring systems. However, very few validation s...
Accelerometry Sleep Stages Deep Learning Heart Rate Photoplethysmography Sleep
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