ArticleNeurobiology of sleep and circadian rhythms2021
Analysis and visualization of sleep stages based on deep neural networks.
Article in Neurobiology of sleep and circadian rhythms, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Analysis of argument structure constructions in a deep recurrent language model.Frontiers in computational neuroscience · 2025Article
- Analysis of argument structure constructions in the large language model BERT.Frontiers in artificial intelligence · 2025Article
- Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception.Brain : a journal of neurology · 2023Review
- Extracting continuous sleep depth from EEG data without machine learning.Neurobiology of sleep and circadian rhythms · 2023Article
- Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts.Scientific reports · 2023Article
- Classification at the accuracy limit: facing the problem of data ambiguity.Scientific reports · 2022Article
- Neural network based successor representations to form cognitive maps of space and language.Scientific reports · 2022Article
- Neural correlates of linguistic collocations during continuous speech perception.Frontiers in psychology · 2022Article
- Intrinsic Noise Improves Speech Recognition in a Computational Model of the Auditory Pathway.Frontiers in neuroscience · 2022Article
- Tinnitus is associated with improved cognitive performance and speech perception-Can stochastic resonance explain?Frontiers in aging neuroscience · 2022Article
- Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages.Communications biology · 2021Article
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Authors and funding
7 authors.
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Abstract
Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages would save human resources and simplify clinical routines. Due to novel open-source software libraries for machine learning, in combination with enormous recent progress in hardware development, a paradigm shift in the field of sleep research towards automatic diagnostics might be imminent. We argue that modern machine learning techniques are not just a tool to perform automatic sleep stage classification, but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, facilitating first assessments on sleep health in terms of sleep-apnea and consequently reduced daytime vigilance. In the following study, we further analyze cortical activity during sleep by determining the probabilities of momentary sleep stages, represented as hypnodensity graphs and then computing vectorial cross-correlations of different EEG channels. We can show that this measure serves to estimate the period length of sleep cycles and thus can help to find disturbances due to pathological conditions.
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