Electroencephalography signal processing: A comprehensive review and analysis of methods and techniques
The electroencephalography (EEG) signal is a noninvasive and complex signal that has
numerous applications in biomedical fields, including sleep and the brain–computer …
numerous applications in biomedical fields, including sleep and the brain–computer …
[HTML][HTML] Review and perspective on sleep-disordered breathing research and translation to clinics
Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep
apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of …
apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of …
[HTML][HTML] An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea
Automatic deep-learning models used for sleep scoring in children with obstructive sleep
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …
MRASleepNet: a multi-resolution attention network for sleep stage classification using single-channel EEG
Objective. Computerized classification of sleep stages based on single-lead
electroencephalography (EEG) signals is important, but still challenging. In this paper, we …
electroencephalography (EEG) signals is important, but still challenging. In this paper, we …
Self‐applied somnography: technical feasibility of electroencephalography and electro‐oculography signal characteristics in sleep staging of suspected sleep …
M Rusanen, H Korkalainen… - Journal of Sleep …, 2024 - Wiley Online Library
Sleep recordings are increasingly being conducted in patients' homes where patients apply
the sensors themselves according to instructions. However, certain sensor types such as …
the sensors themselves according to instructions. However, certain sensor types such as …
A Review on Automated Sleep Study
In recent years, research on automated sleep analysis has witnessed significant growth,
reflecting advancements in understanding sleep patterns and their impact on overall health …
reflecting advancements in understanding sleep patterns and their impact on overall health …
Prediction of the Sleep Apnea Severity Using 2D-Convolutional Neural Networks and Respiratory Effort Signals
The high prevalence of sleep apnea and the limitations of polysomnography have prompted
the investigation of strategies aimed at automated diagnosis using a restricted number of …
the investigation of strategies aimed at automated diagnosis using a restricted number of …
A new automatic sleep stage classification model using swarm intelligence-based hybrid transfer learning architecture
Existing automatic sleep stage classification systems have mostly relied on hand-crafted
features selected from polysomnographic records. To measure the quality of sleep, the …
features selected from polysomnographic records. To measure the quality of sleep, the …
Existing Methodologies, Evaluation Metrics, Research Gaps, and Future Research Trends: A Sleep Stage Classification Framework
The quality of life of the patients is degraded when the person is affected with sleep-related
disorders that include narcolepsy, insomnia, and sleep apnea. The sleep stage classification …
disorders that include narcolepsy, insomnia, and sleep apnea. The sleep stage classification …
Self-applied electrode set provides a clinically feasible solution enabling EEG recording in home sleep apnea testing
Home sleep apnea testing (HSAT) without electroencephalography (EEG) recording is
increasingly used as an alternative to in-laboratory polysomnography for the diagnosis of …
increasingly used as an alternative to in-laboratory polysomnography for the diagnosis of …