AIMC Topic: Polysomnography

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Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram.

Scientific reports Oct 18, 2023
The lateral cephalogram in orthodontics is a valuable screening tool on undetected obstructive sleep apnea (OSA), which can lead to consequences of severe systematic disease. We hypothesized that a deep learning-based classifier might be able to diff...
Radiography Sleep Apnea, Obstructive Polysomnography Deep Learning ROC Curve Humans
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Automatic stridor detection using small training set via patch-wise few-shot learning for diagnosis of multiple system atrophy.

Scientific reports Jul 5, 2023
Stridor is a rare but important non-motor symptom that can support the diagnosis and prediction of worse prognosis in multiple system atrophy. Recording sounds generated during sleep by video-polysomnography is recommended for detecting stridor, but ...
Prognosis Polysomnography Respiratory Sounds Humans Artificial Intelligence Multiple System Atrophy
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Detection of preceding sleep apnea using ECG spectrogram during CPAP titration night: A novel machine-learning and bag-of-features framework.

Journal of sleep research Jul 4, 2023
Obstructive sleep apnea (OSA) has a heavy health-related burden on patients and the healthcare system. Continuous positive airway pressure (CPAP) is effective in treating OSA, but adherence to it is often inadequate. A promising solution is to detect...
Sleep Apnea, Obstructive Polysomnography Continuous Positive Airway Pressure Electrocardiography Support Vector Machine Female Aged Machine Learning Male Algorithms Middle Aged Retrospective Studies Humans Adult
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SLEEP-SEE-THROUGH: Explainable Deep Learning for Sleep Event Detection and Quantification From Wearable Somnography.

IEEE journal of biomedical and health informatics Jun 30, 2023
Evidence is rapidly accumulating that multifactorial nocturnal monitoring, through the coupling of wearable devices and deep learning, may be disruptive for early diagnosis and assessment of sleep disorders. In this work, optical, differential air-pr...
Sleep Snoring Polysomnography Deep Learning Wearable Electronic Devices Apnea Humans
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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...
Sleep Sleep Stages Humans Sleep Wake Disorders Artificial Intelligence Polysomnography
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Self-helped detection of obstructive sleep apnea based on automated facial recognition and machine learning.

Sleep & breathing = Schlaf & Atmung Jun 6, 2023
PURPOSE: The diagnosis of obstructive sleep apnea (OSA) relies on time-consuming and complicated procedures which are not always readily available and may delay diagnosis. With the widespread use of artificial intelligence, we presumed that the combi...
Mass Screening Sleep Apnea, Obstructive Polysomnography Automated Facial Recognition Female Male Humans Surveys and Questionnaires Artificial Intelligence Machine Learning
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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 Electroencephalography Polysomnography Sleep Stages Sleep
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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...
Electroencephalography Sleep Sleep Stages Polysomnography Humans Neural Networks, Computer
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A machine learning approach for the diagnosis of obstructive sleep apnoea using oximetry, demographic and anthropometric data.

Singapore medical journal May 2, 2023
INTRODUCTION: Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings....
Oximetry Demography Polysomnography Humans Machine Learning Neural Networks, Computer Algorithms Adult Sensitivity and Specificity Male Female Aged Sleep Apnea, Obstructive Anthropometry Predictive Value of Tests Middle Aged ROC Curve
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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 Stages Sleep Apnea, Obstructive Humans Sleep Deep Learning Polysomnography
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