AIMC Topic: Polysomnography
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Machine learning-empowered sleep staging classification using multi-modality signals.
BMC medical informatics and decision making
May 6, 2024
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EO...
Expert-level sleep staging using an electrocardiography-only feed-forward neural network.
Computers in biology and medicine
Apr 29, 2024
Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and understanding of brain states. The current gold standard method for sleep stage classification is polysomno...
ZleepAnlystNet: a novel deep learning model for automatic sleep stage scoring based on single-channel raw EEG data using separating training.
Scientific reports
Apr 29, 2024
Numerous models for sleep stage scoring utilizing single-channel raw EEG signal have typically employed CNN and BiLSTM architectures. While these models, incorporating temporal information for sequence classification, demonstrate superior overall per...
Identifying time-resolved features of nocturnal sleep characteristics of narcolepsy using machine learning.
Journal of sleep research
Apr 26, 2024
The differential diagnosis of narcolepsy type 1, a rare, chronic, central disorder of hypersomnolence, is challenging due to overlapping symptoms with other hypersomnolence disorders. While recent years have seen significant growth in our understandi...
Physics-Informed Transfer Learning to Enhance Sleep Staging.
IEEE transactions on bio-medical engineering
Apr 22, 2024
OBJECTIVE: At-home sleep staging using wearable medical sensors poses a viable alternative to in-hospital polysomnography due to its lower cost and lower disruption to the daily lives of patients, especially in the case of long-term monitoring. Machi...
SlumberNet: deep learning classification of sleep stages using residual neural networks.
Scientific reports
Feb 27, 2024
Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture...
Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index.
Annals of clinical and translational neurology
Feb 23, 2024
OBJECTIVE: This longitudinal study investigated potential positive impact of CPAP treatment on brain health in individuals with obstructive sleep Apnea (OSA). To allow this, we aimed to employ sleep electroencephalogram (EEG)-derived brain age index ...
Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.
Sleep medicine
Jan 11, 2024
BACKGROUND: /Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal resp...
A robust deep learning detector for sleep spindles and K-complexes: towards population norms.
Scientific reports
Jan 2, 2024
Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that appear during sleep. Large-scale sleep studies would benefit from precise and robust automatic sleep event detectors, capable of adapting the variabili...
Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders.
Scientific reports
Oct 18, 2023
Deep learning methods have gained significant attention in sleep science. This study aimed to assess the performance of a deep learning-based sleep stage classification model constructed using fewer physiological parameters derived from cardiorespira...
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