ArticleCommunications biology2021
DeepSleep convolutional neural network allows accurate and fast detection of sleep arousal.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Automated sleep stage and event detection algorithms using quality-controlled polysomnography annotations.Sleep advances : a journal of the Sleep Research Society · 2026Article
- CAISR: achieving human-level performance in automated sleep analysis across all clinical sleep metrics.Sleep · 2025Article
- Artificial Intelligence Can Drive Sleep Medicine.Sleep medicine clinics · 2025Review
- State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset.Scientific reports · 2024Article
- Deep convolutional architecture-based hybrid learning for sleep arousal events detection through single-lead EEG signals.Brain and behavior · 2023Article
- A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning.Computational intelligence and neuroscience · 2023Article
- Multilevel Modeling of Joint Damage in Rheumatoid Arthritis.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2022Article
- Asymmetric Predictive Relationships Across Histone Modifications.Nature machine intelligence · 2022Article
- Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning.Journal of neuroscience methods · 2022Article
- Auto-annotating sleep stages based on polysomnographic data.Patterns (New York, N.Y.) · 2022Article
- Fast decoding cell type-specific transcription factor binding landscape at single-nucleotide resolution.Genome research · 2021Article
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Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep arousals are associated with symptoms such as sympathetic activation, non-restorative sleep, and daytime sleepiness. Currently, sleep arousals are mainly annotated by human experts through looking at 30-second epochs (recorded pages) manually, which requires considerable time and effort. Here we present a deep learning approach for automatically segmenting sleep arousal regions based on polysomnographic recordings. Leveraging a specific architecture that 'translates' input polysomnographic signals to sleep arousal labels, this algorithm ranked first in the "You Snooze, You Win" PhysioNet Challenge. We created an augmentation strategy by randomly swapping similar physiological channels, which notably improved the prediction accuracy. Our algorithm enables fast and accurate delineation of sleep arousal events at the speed of 10 seconds per sleep recording. This computational tool would greatly empower the scoring process in clinical settings and accelerate studies on the impact of arousals.
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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.