ArticleJournal of computational neuroscience2011
A continuous mapping of sleep states through association of EEG with a mesoscale cortical model.
Article in Journal of computational neuroscience, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 23 citations in OpenAlex.
- Article
- Transient neocortical gamma oscillations induced by neuronal response modulation.Journal of computational neuroscience · 2020Article
- Human seizures couple across spatial scales through travelling wave dynamics.Nature communications · 2017Article
- Characterizing Awake and Anesthetized States Using a Dimensionality Reduction Method.Journal of medical systems · 2016Article
- A probabilistic method for determining cortical dynamics during seizures.Journal of computational neuroscience · 2015Article
- A probabilistic framework for a physiological representation of dynamically evolving sleep state.Journal of computational neuroscience · 2014Article
- Emergence from general anesthesia and the sleep-manifold.Frontiers in systems neuroscience · 2014Article
- Inter-hemispheric oscillations in human sleep.PloS one · 2012Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time.
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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.