ReviewBrain sciences2021
A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals.
Review in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 33 citations in OpenAlex.
- Automated deep learning-based arousal detection agrees with ten expert scorers in unseen data.Sleep · 2026Article
- Hypnogram-Driven Automatic Sleep Staging and a Quality-Index Assessment Through a Two-Stage LSTM-DNN Ensemble Learning Approach Using Multi-Biosignal Features for Sleep Disorder Detection.Sensors (Basel, Switzerland) · 2026Article
- Earth Vertical Motions Disrupt Sleep and Next Day Performance.Nature and science of sleep · 2026Article
- Sleep Instability and Severe Obstructive Sleep Apnea are Associated with Carotid Atherosclerotic Burden but Not Plaque Vulnerability in Acute Ischemic Stroke.Nature and science of sleep · 2026Article
- Internet-delivered cognitive behavioral therapy for insomnia: The future of insomnia treatment with large language models.Sleep medicine: X · 2025Review
- State-of-the-art wearable sensors for cardiovascular health: a review.NPJ cardiovascular health · 2025Review
- Article
- Detecting arousals and sleep from respiratory inductance plethysmography.Sleep & breathing = Schlaf & Atmung · 2025Article
- Artificial Intelligence Can Drive Sleep Medicine.Sleep medicine clinics · 2025Review
- SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics.Interdisciplinary sciences, computational life sciences · 2024Article
- State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset.Scientific reports · 2024Article
- Obstructive sleep apnea event detection using explainable deep learning models for a portable monitor.Frontiers in neuroscience · 2023Article
- Measuring depression severity based on facial expression and body movement using deep convolutional neural network.Frontiers in psychiatry · 2022Article
- Instability of non-REM sleep in older women evaluated by sleep-stage transition and envelope analyses.Frontiers in aging neuroscience · 2022Article
- Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients.Frontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.