AIMC Topic: Polysomnography

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Towards interpretable sleep stage classification with a multi-stream fusion network.

BMC medical informatics and decision making
Sleep stage classification is a significant measure in assessing sleep quality and diagnosing sleep disorders. Many researchers have investigated automatic sleep stage classification methods and achieved promising results. However, these methods igno...

Clinical-level screening of sleep apnea syndrome with single-lead ECG alone is achievable using machine learning with appropriate time windows.

Sleep & breathing = Schlaf & Atmung
PURPOSE: To establish a simple and noninvasive screening test for sleep apnea (SA) that imposes less burden on potential patients. The specific objective of this study was to verify the effectiveness of past and future single-lead electrocardiogram (...

Detecting arousals and sleep from respiratory inductance plethysmography.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Accurately identifying sleep states (REM, NREM, and Wake) and brief awakenings (arousals) is essential for diagnosing sleep disorders. Polysomnography (PSG) is the gold standard for such assessments but is costly and requires overnight monit...

CHMMConvScaleNet: a hybrid convolutional neural network and continuous hidden Markov model with multi-scale features for sleep posture detection.

Scientific reports
Sleep posture, a vital aspect of sleep wellness, has become a crucial focus in sleep medicine. Studies show that supine posture can lead to a higher occurrence of obstructive sleep apnea, while lateral posture might reduce it. For bedridden patients,...

Automated Sleep Staging in Epilepsy Using Deep Learning on Standard Electroencephalogram and Wearable Data.

Journal of sleep research
Automated sleep staging on wearable data could improve our understanding and management of epilepsy. This study evaluated sleep scoring by a deep learning model on 223 night-sleep recordings from 50 patients measured in the hospital with an electroen...

Separating obstructive and central respiratory events during sleep using breathing sounds: Utilizing transfer learning on deep convolutional networks.

Sleep medicine
Sleep apnea diagnosis relies on polysomnography (PSG), which is resource-intensive and requires manual analysis to differentiate obstructive sleep apnea (OSA) from central sleep apnea (CSA). Existing portable devices, while valuable in detecting slee...

Deciphering Insomnia: Benchmarking Automated Sleep Staging Algorithms for Complex Sleep Disorders.

Journal of sleep research
Polysomnography (PSG) is essential for diagnosing sleep disorders, but its manual interpretation is labor-intensive. Automated sleep staging algorithms are promising, yet their utility in complex sleep disorders such as insomnia remains uncertain. Th...

Distinguishing severe sleep apnea from habitual snoring using a neck-wearable piezoelectric sensor and deep learning: A pilot study.

Computers in biology and medicine
This study explores the development of a deep learning model using a neck-wearable piezoelectric sensor to accurately distinguish severe sleep apnea syndrome (SAS) from habitual snoring, addressing the underdiagnosis of SAS in adults. From 2018 to 20...

Deep Learning-Based Event Counting for Apnea-Hypopnea Index Estimation Using Recursive Spiking Neural Networks.

IEEE transactions on bio-medical engineering
OBJECTIVE: To develop a novel method for improved screening of sleep apnea in home environments, focusing on reliable estimation of the Apnea-Hypopnea Index (AHI) without the need for highly precise event localization.

GraphSleepFormer: a multi-modal graph neural network for sleep staging in OSA patients.

Journal of neural engineering
Obstructive sleep apnea (OSA) is a prevalent sleep disorder. Accurate sleep staging is one of the prerequisites in the study of sleep-related disorders and the evaluation of sleep quality. We introduce a novel GraphSleepFormer (GSF) network designed ...