The "Twin Peaks" method of automated Spike-Wave detection: A two-step, two-criteria Matlab application.

Autor: Iotchev IB; Department of Ethology, Eötvös Loránd University ELTE, Pázmány Péter sétány 1/c, Budapest 1117, Hungary. Electronic address: ivaylo.iotchev@gmail.com., Perevozniuk DA; Institute of the Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Butlerova Str., 5A, Moscow 117485, Russian Federation., Lazarenko I; Institute of the Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Butlerova Str., 5A, Moscow 117485, Russian Federation., Perescis MFJ; HAS Green Academy, Onderwijsboulevard 221, 's-Hertogenbosch 5223 DE, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Thomas van Aquinostraat 4, Nijmegen 6525 GD, the Netherlands., Sitnikova E; Institute of the Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Butlerova Str., 5A, Moscow 117485, Russian Federation., van Luijtelaar G; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Thomas van Aquinostraat 4, Nijmegen 6525 GD, the Netherlands.
Jazyk: angličtina
Zdroj: Journal of neuroscience methods [J Neurosci Methods] 2024 Sep; Vol. 409, pp. 110199. Date of Electronic Publication: 2024 Jun 17.
DOI: 10.1016/j.jneumeth.2024.110199
Abstrakt: Background: There are many automated spike-wave discharge detectors, but the known weaknesses of otherwise good methods and the varying working conditions of different research groups (mainly the access to hardware and software) invite further exploration into alternative approaches.
New Method: The algorithm combines two criteria, one in the time-domain and one in the frequency-domain, exploiting morphological asymmetry and the presence of harmonics, respectively. The time-domain criterion is additionally adjusted by normal modelling between the first and second iterations.
Results: We report specificity, sensitivity and accuracy values for 20 recordings from 17 mature, male WAG/Rij rats. In addition, performance was preliminary tested with different hormones, pharmacological injections and species (mice) in a smaller sample. Accuracy and specificity were consistently above 91 %. The number of automatically detected spike-wave discharges was strongly correlated with the numbers derived from visual inspection. Sensitivity varied more strongly than specificity, but high values were observed in both rats and mice.
Comparison With Existing Methods: The algorithm avoids low-voltage movement artifacts, displays a lower false positive rate than many predecessors and appears to work across species, i.e. while designed initially with data from the WAG/Rij rat, the algorithm can pick up seizure activity in the mouse of considerably lower inter-spike frequency. Weaknesses of the proposed method include a lower sensitivity than several predecessors.
Conclusion: The algorithm excels in being a selective and flexible (based on e.g. its performance across rats and mice) spike-wave discharge detector. Future work could attempt to increase the sensitivity of this approach.
Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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