ArticleFrontiers in human neuroscience2015
Automatic sleep spindle detection: benchmarking with fine temporal resolution using open science tools.
Article in Frontiers in human neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Identifying sleep spindles with multichannel EEG and classification optimization.Computers in biology and medicine · 2017Trial
- The DYNAM-O Toolbox: Characterizing Individualized Neural Signatures in Sleep EEG.bioRxiv : the preprint server for biology · 2026Article
- Meta-Analysis of Face-Validity Indicators in Automatically Detected Dog Sleep Spindles.Neuroinformatics · 2026Article
- Article
- Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An Open-Source Platform for Automatic Sleep Staging of Rodent Electrocorticographic and Electromyographic Data.Journal of biological rhythms · 2025Article
- SPINDILOMETER: a model describing sleep spindles on EEG signals for polysomnography.Physical and engineering sciences in medicine · 2024Article
- A personalized semi-automatic sleep spindle detection (PSASD) framework.Journal of neuroscience methods · 2024Article
- Automatic sleep spindles identification and classification with multitapers and convolution.Sleep · 2024Article
- Factors affecting discrepancies between scorers in manual sleep spindle detections in single-channel electroencephalography in young adult males.Frontiers in sleep · 2024Article
- An examination of sleep spindle metrics in the Sleep Heart Health Study: superiority of automated spindle detection over total sigma power in assessing age-related spindle decline.BMC neurology · 2023Article
- Selection of stimulus parameters for enhancing slow wave sleep events with a neural-field theory thalamocortical model.PLoS computational biology · 2021Article
- A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.Scientific reports · 2021Article
- Reliability of Family Dogs' Sleep Structure Scoring Based on Manual and Automated Sleep Stage Identification.Animals : an open access journal from MDPI · 2020Article
- A deep learning approach for real-time detection of sleep spindles.Journal of neural engineering · 2019Article
- Delay differential analysis for dynamical sleep spindle detection.Journal of neuroscience methods · 2019Article
- Different Patterns of Sleep-Dependent Procedural Memory Consolidation in Vipassana Meditation Practitioners and Non-meditating Controls.Frontiers in psychology · 2019Article
- EEG Transients in the Sigma Range During non-REM Sleep Predict Learning in Dogs.Scientific reports · 2017Article
- Individual Differences in Frequency and Topography of Slow and Fast Sleep Spindles.Frontiers in human neuroscience · 2017Article
- Sleep: An Open-Source Python Software for Visualization, Analysis, and Staging of Sleep Data.Frontiers in neuroinformatics · 2017Article
- Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms.Frontiers in human neuroscience · 2017Article
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2 authors.
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Abstract
Sleep spindle properties index cognitive faculties such as memory consolidation and diseases such as major depression. For this reason, scoring sleep spindle properties in polysomnographic recordings has become an important activity in both research and clinical settings. The tediousness of this manual task has motivated efforts for its automation. Although some progress has been made, increasing the temporal accuracy of spindle scoring and improving the performance assessment methodology are two aspects needing more attention. In this paper, four open-access automated spindle detectors with fine temporal resolution are proposed and tested against expert scoring of two proprietary and two open-access databases. Results highlight several findings: (1) that expert scoring and polysomnographic databases are important confounders when comparing the performance of spindle detectors tested using different databases or scorings; (2) because spindles are sparse events, specificity estimates are potentially misleading for assessing automated detector performance; (3) reporting the performance of spindle detectors exclusively with sensitivity and specificity estimates, as is often seen in the literature, is insufficient; including sensitivity, precision and a more comprehensive statistic such as Matthew's correlation coefficient, F1-score, or Cohen's κ is necessary for adequate evaluation; (4) reporting statistics for some reasonable range of decision thresholds provides a much more complete and useful benchmarking; (5) performance differences between tested automated detectors were found to be similar to those between available expert scorings; (6) much more development is needed to effectively compare the performance of spindle detectors developed by different research teams. Finally, this work clarifies a long-standing but only seldomly posed question regarding whether expert scoring truly is a reliable gold standard for sleep spindle assessment.
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