AIMC Topic: Polysomnography

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An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
STUDY OBJECTIVES: Although sleep is frequently disrupted in the pediatric intensive care unit, it is currently not possible to perform real-time sleep monitoring at the bedside. In this study, spectral band powers of electroencephalography data are u...

Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep st...

Standardized image-based polysomnography database and deep learning algorithm for sleep-stage classification.

Sleep
STUDY OBJECTIVES: Polysomnography (PSG) scoring is labor-intensive, subjective, and often ambiguous. Recently several deep learning (DL) models for automated sleep scoring have been developed, they are tied to a fixed amount of input channels and res...

Identification of Sleep Patterns via Clustering of Hypnodensities.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Sleep patterns vary widely between individuals. We explore methods for identifying populations exhibiting similar sleep patterns in an automated fashion using polysomnography data. Our novel approach applies unsupervised machine learning algorithms t...

Enhancing OSA Assessment with Explainable AI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Explainable Artificial Intelligence (xAI) is a rapidly growing field that focuses on making deep learning models interpretable and understandable to human decision-makers. In this study, we introduce xAAEnet, a novel xAI model applied to the assessme...

End-to-end Deep Learning of Polysomnograms for Classification of REM Sleep Behavior Disorder.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Rapid eye movement (REM) sleep behavior disorder (RBD) is parasomnia and a prodromal manifestation of Parkinson's disease. The current diagnostic method relies on manual scoring of polysomnograms (PSGs), a procedure that is time and effort intensive,...

Classification of Sleep-Wake State in Ballistocardiogram system based on Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Sleep state classification is essential for managing and comprehending sleep patterns, and it is usually the first step in identifying sleep disorders. Polysomnography (PSG), the gold standard, is intrusive and inconvenient for regular/long-term slee...

Deep-Learning based Sleep Apnea Detection using SpO2 and Pulse Rate.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This work presents automated apnea event de-tection using blood oxygen saturation (SpO2) and pulse rate (PR), conveniently recorded with a pulse oximeter. A large, diverse cohort of patients (n=8068, age≄40 years) from the sleep heart health study da...

Deep-Learning Model Based on Convolutional Neural Networks to Classify Apnea-Hypopnea Events from the Oximetry Signal.

Advances in experimental medicine and biology
Automated analysis of the blood oxygen saturation (SpO) signal from nocturnal oximetry has shown usefulness to simplify the diagnosis of obstructive sleep apnea (OSA), including the detection of respiratory events. However, the few preceding studies ...