AIMC Topic: Sleep Stages
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A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture.
IEEE transactions on pattern analysis and machine intelligence
Jun 5, 2024
In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decis...
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...
Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.
IEEE transactions on neural networks and learning systems
May 2, 2024
Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary charact...
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...
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...
U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging.
Computers in biology and medicine
Feb 23, 2024
With the increasing prevalence of machine learning in critical fields like healthcare, ensuring the safety and reliability of these systems is crucial. Estimating uncertainty plays a vital role in enhancing reliability by identifying areas of high an...
Timing matters for accurate identification of the epileptogenic zone.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Feb 18, 2024
OBJECTIVE: Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intra...
Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice.
Sleep medicine reviews
Jan 11, 2024
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence tech...
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