Sleep spindles (SS) and slow waves (SW) serve as indicators of the integrity of thalamocortical connections, which are often compromised in individuals with autism spectrum disorder (ASD). Transcranial magnetic stimulation (TMS) can modulate brain ac...
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Nov 15, 2024
The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment of pediatric sleep disorders. However, most existing sleep staging methods rely on one-dimensional r...
OBJECTIVE: This study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including p...
Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this...
Cyclic alternating patterns (CAP) occur in electroencephalogram (EEG) signals during non-rapid eye movement sleep. The analysis of CAP can offer insights into various sleep disorders. The first step is the identification of phases A and B for the CAP...
Numerous automatic sleep stage classification systems have been developed, but none have become effective assistive tools for sleep technicians due to issues with generalization. Four key factors hinder the generalization of these models are instrume...
IEEE journal of biomedical and health informatics
Sep 5, 2024
Sleep staging plays a critical role in evaluating the quality of sleep. Currently, most studies are either suffering from dramatic performance drops when coping with varying input modalities or unable to handle heterogeneous signals. To handle hetero...
Computer methods and programs in biomedicine
Sep 2, 2024
BACKGROUND AND OBJECTIVE: Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer from them. Numerous sleep staging models have been proposed recently, but most of them have not fully e...
Over the past decades, feature-based statistical machine learning and deep neural networks have been extensively utilized for automatic sleep stage classification (ASSC). Feature-based approaches offer clear insights into sleep characteristics and re...
BACKGROUND: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wa...
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