ArticleJournal of neuroscience methods2019
Delay differential analysis for dynamical sleep spindle detection.
Article in Journal of neuroscience methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Transcranial Magnetic Stimulation Facilitates Neural Speech Decoding.Brain sciences · 2024Article
- Decoding imagined speech with delay differential analysis.Frontiers in human neuroscience · 2024Article
- Network-motif delay differential analysis of brain activity during seizures.Chaos (Woodbury, N.Y.) · 2023Article
- Predicting chromosomal compartments directly from the nucleotide sequence with DNA-DDA.Briefings in bioinformatics · 2023Article
- Quantifying Phase-Amplitude Modulation in Neural Data.... Annual Conference on Information Sciences and Systems. Annual Conference on Information Sciences and Systems · 2023Article
- Precision multidimensional neural population code recovered from single intracellular recordings.Scientific reports · 2020Article
- Nonlinear dynamics underlying sensory processing dysfunction in schizophrenia.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Interpretation of the Precision Matrix and Its Application in Estimating Sparse Brain Connectivity during Sleep Spindles from Human Electrocorticography Recordings.Neural computation · 2017Article
Corrections and comments
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Authors and funding
10 authors at 7 institutions in 2 countries.
Funding
Abstract
backgroundSleep spindles are involved in memory consolidation and other cognitive functions. Numerous automated methods for detection of spindles have been proposed; most of these rely on spectral analysis in some form. However, none of these approaches are ideal, and novel approaches to the problem could provide additional insights. NEW
methodHere, we apply delay differential analysis (DDA), a time-domain technique based on nonlinear dynamics to detect sleep spindles in human intracranial sleep data, including laminar electrode, stereoelectroencephalogram (sEEG), and electrocorticogram (ECoG) recordings.
resultsWe show that this approach is computationally fast, generalizable, requires minimal preprocessing, and provides excellent agreement with human scoring. COMPARISON WITH EXISTING
methodsWe compared the method with established methods on a set of intracranial recordings and this method provided the highest agreement with human expert scoring when evaluated with F
conclusionsThis additional, non-frequency-based perspective could prove particularly useful for certain atypical spindles, or identifying spindles of different types.
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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.