AIMC Topic: Polysomnography

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Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Aug 14, 2024
Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed, and in particular, deep-learning based algorithms have achieved performance ...
Deep Learning Electroencephalography Polysomnography Sleep Stages Humans Algorithms Neural Networks, Computer Female Machine Learning Adult Male
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Automated remote sleep monitoring needs uncertainty quantification.

Journal of sleep research Aug 7, 2024
Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage c...
Machine Learning Female Reproducibility of Results Aged Humans Polysomnography Sleep Wake Disorders Male Uncertainty Wearable Electronic Devices Sleep Stages Electroencephalography Accelerometry
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Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sensors (Basel, Switzerland) Jul 25, 2024
Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are c...
Posture Humans Sleep Apnea, Obstructive Adult Neural Networks, Computer Respiratory Rate Male Heart Rate Female Polysomnography Monitoring, Physiologic Sleep
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Study on the classification of sleep stages in EEG signals based on DoubleLinkSleepCLNet.

Sleep & breathing = Schlaf & Atmung Jul 24, 2024
PURPOSE: The classification of sleep stages based on Electroencephalogram (EEG) changes has significant implications for evaluating sleep quality and sleep status. Most polysomnography (PSG) systems have a limited number of channels and do not achiev...
Signal Processing, Computer-Assisted Polysomnography Sleep Stages Adult Humans Male Neural Networks, Computer Deep Learning Electroencephalography
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Improved sleep stage predictions by deep learning of photoplethysmogram and respiration patterns.

Computers in biology and medicine Jul 20, 2024
Sleep staging is a crucial tool for diagnosing and monitoring sleep disorders, but the standard clinical approach using polysomnography (PSG) in a sleep lab is time-consuming, expensive, uncomfortable, and limited to a single night. Advancements in s...
Middle Aged Deep Learning Photoplethysmography Signal Processing, Computer-Assisted Respiration Polysomnography Sleep Stages Humans Female Adult Male
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Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals.

Journal of sleep research Jul 18, 2024
Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obst...
Polysomnography Electrocardiography Signal Processing, Computer-Assisted Sleep Apnea, Obstructive Reproducibility of Results Aged Humans Adult Female Male Middle Aged Severity of Illness Index Deep Learning
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A novel deep learning model based on transformer and cross modality attention for classification of sleep stages.

Journal of biomedical informatics Jul 18, 2024
The classification of sleep stages is crucial for gaining insights into an individual's sleep patterns and identifying potential health issues. Employing several important physiological channels in different views, each providing a distinct perspecti...
Polysomnography Sleep Stages Male Humans Deep Learning Neural Networks, Computer Electroencephalography Algorithms Signal Processing, Computer-Assisted
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Comparison of automated deep neural network against manual sleep stage scoring in clinical data.

Computers in biology and medicine Jul 18, 2024
OBJECTIVE: To compare the accuracy and generalizability of an automated deep neural network and the Philip Sleepware G3™ Somnolyzer system (Somnolyzer) for sleep stage scoring using American Academy of Sleep Medicine (AASM) guidelines.
Electrooculography Sleep Stages Middle Aged Humans Signal Processing, Computer-Assisted Neural Networks, Computer Polysomnography Female Aged Adult Male
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A Deep Transfer Learning Approach for Sleep Stage Classification and Sleep Apnea Detection Using Wrist-Worn Consumer Sleep Technologies.

IEEE transactions on bio-medical engineering Jul 18, 2024
Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using w...
Humans Female Aged Wearable Electronic Devices Signal Processing, Computer-Assisted Sleep Apnea Syndromes Wrist Adult Male Middle Aged Deep Learning Accelerometry Polysomnography Sleep Stages
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ECGAN-Assisted ResT-Net Based on Fuzziness for OSA Detection.

IEEE transactions on bio-medical engineering Jul 18, 2024
OBJECTIVE: Growing attention has been paid recently to electrocardiogram (ECG) based obstructive sleep apnea (OSA) detection, with some progresses been made on this topic. However, the lack of data, low data quality, and incomplete data labeling hind...
Sleep Apnea, Obstructive Polysomnography Female Adult Male Middle Aged Deep Learning Electrocardiography Signal Processing, Computer-Assisted Humans Neural Networks, Computer Algorithms Databases, Factual
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