ArticleScientific reports2019
MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Highly mechanically stable PEDOT:PSS/PDA-modified microelectrode arrays reveal state-specific dynamic neural activity across sleep-wake.Microsystems & nanoengineering · 2026Article
- Heart rate and sleep history encode ultradian REM sleep timing.Current biology : CB · 2026Article
- Neural network architectures and normalization techniques for automated sleep stage classification using rodent EEG and EMG signals.PloS one · 2026Article
- REST-a deep learning tool for automated mouse sleep stage classification.Sleep advances : a journal of the Sleep Research Society · 2026Article
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- Advances and challenges in experimental models of posttraumatic epilepsy for therapeutic interventions.Pharmacological reviews · 2025Review
- Probability estimation of narcolepsy type 1 in DTA mice using unlabeled EEG and EMG data.Sleep advances : a journal of the Sleep Research Society · 2025Article
- LG-Sleep: Local and Global Temporal Dependencies for Mice Sleep Scoring.IEEE sensors letters · 2025Article
- Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice.Scientific reports · 2025Article
- MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice.Biosensors · 2024Article
- Optogenetic Brain-Computer Interfaces.Bioengineering (Basel, Switzerland) · 2024Review
- SlumberNet: deep learning classification of sleep stages using residual neural networks.Scientific reports · 2024Article
- Transcranial Direct Current Stimulation (tDCS) Ameliorates Stress-Induced Sleep Disruption via Activating Infralimbic-Ventrolateral Preoptic Projections.Brain sciences · 2024Article
- Sleep-Deep-Learner is taught sleep-wake scoring by the end-user to complete each record in their style.Sleep advances : a journal of the Sleep Research Society · 2024Article
- Translational models of stress and resilience: An applied neuroscience methodology review.Neuroscience applied · 2024Review
- Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.PLoS computational biology · 2024Article
- Sleep-Deep-Net learns sleep wake scoring from the end-user and completes each record in their style.bioRxiv : the preprint server for biology · 2023Article
- Article
- IntelliSleepScorer, a software package with a graphic user interface for automated sleep stage scoring in mice based on a light gradient boosting machine algorithm.Scientific reports · 2023Article
- General-Purpose Ultrasound Neuromodulation System for Chronic, Closed-Loop Preclinical Studies in Freely Behaving Rodents.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated sleep stage scoring for mice is in high demand for sleep research, since manual scoring requires considerable human expertise and efforts. The existing automated scoring methods do not provide the scoring accuracy required for practical use. In addition, the performance of such methods has generally been evaluated using rather small-scale datasets, and their robustness against individual differences and noise has not been adequately verified. This research proposes a novel automated scoring method named "MC-SleepNet", which combines two types of deep neural networks. Then, we evaluate its performance using a large-scale dataset that contains 4,200 biological signal records of mice. The experimental results show that MC-SleepNet can automatically score sleep stages with an accuracy of 96.6% and kappa statistic of 0.94. In addition, we confirm that the scoring accuracy does not significantly decrease even if the target biological signals are noisy. These results suggest that MC-SleepNet is very robust against individual differences and noise. To the best of our knowledge, evaluations using such a large-scale dataset (containing 4,200 records) and high scoring accuracy (96.6%) have not been reported in previous related studies.
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