ArticleNature and science of sleep2015
An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters.
Article in Nature and science of sleep, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice.Biosensors · 2024Article
- Automatic sleep-wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine.CNS neuroscience & therapeutics · 2024Article
- SlumberNet: deep learning classification of sleep stages using residual neural networks.Scientific reports · 2024Article
- Sex differences in body temperature and neural power spectra in response to repeated restraint stress.Stress (Amsterdam, Netherlands) · 2024Article
- Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques.Frontiers in artificial intelligence · 2024Article
- Discriminating rapid eye movement sleep from wakefulness by analyzing high frequencies from single-channel EEG recordings in mice.Scientific reports · 2023Article
- Article
- Stability of neocortical synapses across sleep and wake states during the critical period in rats.eLife · 2021Article
- Article
- MC-SleepNet: Large-scale Sleep Stage Scoring in Mice by Deep Neural Networks.Scientific reports · 2019Article
- SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species.PLoS computational biology · 2019Article
- Robust, automated sleep scoring by a compact neural network with distributional shift correction.PloS one · 2019Article
- Validation of 'Somnivore', a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data.Frontiers in neuroscience · 2019Article
- Sleep disruption elevates oxidative stress in parvalbumin-positive cells of the rat cerebral cortex.Sleep · 2019Article
- A novel unsupervised analysis of electrophysiological signals reveals new sleep substages in mice.PLoS biology · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
introductionRodent sleep research uses electroencephalography (EEG) and electromyography (EMG) to determine the sleep state of an animal at any given time. EEG and EMG signals, typically sampled at >100 Hz, are segmented arbitrarily into epochs of equal duration (usually 2-10 seconds), and each epoch is scored as wake, slow-wave sleep (SWS), or rapid-eye-movement sleep (REMS), on the basis of visual inspection. Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.
methodsWe developed a semiautomated state-scoring procedure that uses a combination of principal component analysis and naïve Bayes classification, with the EEG and EMG as inputs. We validated this algorithm against human-scored sleep-state scoring of data from C57BL/6J and BALB/CJ mice. We then applied a general homeostatic model to characterize the state-dependent dynamics of sleep slow-wave activity and cerebral glycolytic flux, measured as lactate concentration.
resultsMore than 89% of epochs scored as wake or SWS by the human were scored as the same state by the machine, whether scoring in 2-second or 10-second epochs. The majority of epochs scored as REMS by the human were also scored as REMS by the machine. However, of epochs scored as REMS by the human, more than 10% were scored as SWS by the machine and 18 (10-second epochs) to 28% (2-second epochs) were scored as wake. These biases were not strain-specific, as strain differences in sleep-state timing relative to the light/dark cycle, EEG power spectral profiles, and the homeostatic dynamics of both slow waves and lactate were detected equally effectively with the automated method or the manual scoring method. Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification.
conclusionsMachine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies. Automated scoring is an efficient alternative to visual inspection in studies of strain differences in sleep and the temporal dynamics of sleep-related physiological parameters.
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