ArticleJournal of neural engineering2019
A deep learning approach for real-time detection of sleep spindles.
Article in Journal of neural engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Closed-Loop Feedback Techniques in Sleep Studies-Related Issues and Future Directions.Sensors (Basel, Switzerland) · 2020Pooled it
- From sleep staging to spindle detection: a case study on end-to-end automated sleep analysis.Scientific reports · 2026Article
- Oscillatory-Quality of sleep spindles links brain state with sleep regulation and function.Science advances · 2024Article
- 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
- A machine learning toolbox for the analysis of sharp-wave ripples reveals common waveform features across species.Communications biology · 2024Article
- A robust deep learning detector for sleep spindles and K-complexes: towards population norms.Scientific reports · 2024Article
- Classification and transfer learning of sleep spindles based on convolutional neural networks.Frontiers in neuroscience · 2024Article
- Networks through the lens of high-frequency oscillations.Frontiers in network physiology · 2024Review
- A machine learning toolbox for the analysis of sharp-wave ripples reveal common features across species.bioRxiv : the preprint server for biology · 2023Article
- Sleep spindles, stress and PTSD: The state of the science and future directions.Neurobiology of stress · 2023Article
- [Sleep spindles-Function, detection and use as biomarker for diagnostics in psychiatry].Der Nervenarzt · 2022Review
- 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
- The Portiloop: A deep learning-based open science tool for closed-loop brain stimulation.PloS one · 2022Article
- Effect of a Recliner Chair with Rocking Motions on Sleep Efficiency.Sensors (Basel, Switzerland) · 2021Article
- Fibromyalgia: Pathogenesis, Mechanisms, Diagnosis and Treatment Options Update.International journal of molecular sciences · 2021Review
- A lightweight convolutional neural network for assessing an EEG risk marker for sudden unexpected death in epilepsy.BMC medical informatics and decision making · 2020Article
- Deep Learning in Physiological Signal Data: A Survey.Sensors (Basel, Switzerland) · 2020Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
objectiveSleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications. APPROACH: Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel. While the majority of spindle detection methods are used for off-line applications, our method is well suited for online applications. MAIN
resultsCompared with other spindle detection methods, SpindleNet achieves superior detection accuracy and speed, as demonstrated in two publicly available expert-validated EEG sleep spindle datasets. Our real-time detection of spindle onset achieves detection latencies of 150-350 ms (~two-three spindle cycles) and retains excellent performance under low EEG sampling frequencies and low signal-to-noise ratios. SpindleNet has good generalization across different sleep datasets from various subject groups of different ages and species. SIGNIFICANCE: SpindleNet is ultra-fast and scalable to multichannel EEG recordings, with an accuracy level comparable to human experts, making it appealing for long-term sleep monitoring and closed-loop neuroscience experiments.
Indexed as
Identifiers
What OpenQuestion holds
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.