ReviewNeural plasticity2016
Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods.
Review in Neural plasticity, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Identifying sleep spindles with multichannel EEG and classification optimization.Computers in biology and medicine · 2017Trial
- From sleep staging to spindle detection: a case study on end-to-end automated sleep analysis.Scientific reports · 2026Article
- Article
- Discovering EEG biomarkers of Lennox-Gastaut syndrome through unsupervised time-frequency analysis.Epilepsia · 2025Article
- A robust deep learning detector for sleep spindles and K-complexes: towards population norms.Scientific reports · 2024Article
- Sleep spindles, stress and PTSD: The state of the science and future directions.Neurobiology of stress · 2023Article
- Advanced sleep spindle identification with neural networks.Scientific reports · 2022Article
- Spike-Wave Seizures, NREM Sleep and Micro-Arousals in WAG/Rij Rats with Genetic Predisposition to Absence Epilepsy: Developmental Aspects.Life (Basel, Switzerland) · 2022Article
- Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings.Clocks & sleep · 2020Article
- A mechanism for learning with sleep spindles.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Review
- Memory quality modulates the effect of aging on memory consolidation during sleep: Reduced maintenance but intact gain.NeuroImage · 2020Article
- Autistic Traits Are Associated With Decreased Activity of Fast Sleep Spindles During Adolescence.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2019Article
- Precise Slow Oscillation-Spindle Coupling Promotes Memory Consolidation in Younger and Older Adults.Scientific reports · 2019Article
- Individual spindle detection and analysis in high-density recordings across the night and in thalamic stroke.Scientific reports · 2018Article
- Abnormal coherence and sleep composition in children with Angelman syndrome: a retrospective EEG study.Molecular autism · 2018Article
- Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain.Frontiers in neuroscience · 2018Article
- Circadian preference towards morningness is associated with lower slow sleep spindle amplitude and intensity in adolescents.Scientific reports · 2017Article
- Dynamic coupling between slow waves and sleep spindles during slow wave sleep in humans is modulated by functional pre-sleep activation.Scientific reports · 2017Article
- Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource.Nature communications · 2017Article
- Developmental Changes in Sleep Oscillations during Early Childhood.Neural plasticity · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a number of sleep (isolation from exteroceptive stimuli, memory consolidation) and individual characteristics (intellectual quotient). Oddly enough, the definition of a spindle is both incomplete and restrictive. In consequence, there is no consensus about how to detect spindles. Visual scoring is cumbersome and user dependent. To analyze spindle activity in a more robust way, automatic sleep spindle detection methods are essential. Various algorithms were developed, depending on individual research interest, which hampers direct comparisons and meta-analyses. In this review, sleep spindle is first defined physically and topographically. From this general description, we tentatively extract the main characteristics to be detected and analyzed. A nonexhaustive list of automatic spindle detection methods is provided along with a description of their main processing principles. Finally, we propose a technique to assess the detection methods in a robust and comparable way.
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