ArticleFrontiers in neuroscience2023
A comparative analysis of sleep spindle characteristics of sleep-disordered patients and normal subjects.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- A Systematic Review and Meta-Analysis of Biological Sex Differences in Sleep Spindles and Slow Wave Activity in Adults With and Without Insomnia.Journal of sleep research · 2026Pooled it
- Article
- The DYNAM-O Toolbox: Characterizing Individualized Neural Signatures in Sleep EEG.bioRxiv : the preprint server for biology · 2026Article
- A case report of atypical type E botulism.World journal of emergency medicine · 2026Article
- Music-evoked EEG signals for mental health assessment using two- and three-class classification.Frontiers in systems neuroscience · 2026Article
- Analysis of the correlation between sleep spindles and cognitive impairment in patients with ischemic stroke.Scientific reports · 2025Article
- Competitive roles of slow/delta oscillation-nesting-mediated sleep disruption under acute methamphetamine exposure in monkeys.Journal of Zhejiang University. Science. B · 2025Article
- Article
- Anesthetic spindles serve as EEG markers of the depth variations in anesthesia induced by multifarious general anesthetics in mouse experiments.Frontiers in pharmacology · 2024Article
- Automatic epileptic seizure detection based on persistent homology.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spindles differ in density, amplitude, and frequency, and these variations reflect different physiological processes. Sleep disorders are characterized by difficulty in falling asleep and maintaining sleep. In this study, we proposed a new spindle wave detection algorithm, which was more effective compared with traditional detection algorithms such as wavelet algorithm. Besides, we recorded EEG data from 20 subjects with sleep disorders and 10 normal subjects, and then we compared the spindle characteristics of sleep-disordered subjects and normal subjects (those without any sleep disorder) to assess the spindle activity during human sleep. Specifically, we scored 30 subjects on the Pittsburgh Sleep Quality Index and then analyzed the association between their sleep quality scores and spindle characteristics, reflecting the effect of sleep disorders on spindle characteristics. We found a significant correlation between the sleep quality score and spindle density (
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