PUBLICATION
Automated analysis of brain activity for seizure detection in zebrafish models of epilepsy
- Authors
- Hunyadi, B., Siekierska, A., Sourbron, J., Copmans, D., de Witte, P.A.M.
- ID
- ZDB-PUB-170605-3
- Date
- 2017
- Source
- Journal of Neuroscience Methods 287: 13-24 (Journal)
- Registered Authors
- Keywords
- Animal model, Automated analysis, Classification, Epilepsy, Local field potential (LFP), Machine learning, SVM, Seizure detection, Zebrafish larvae
- MeSH Terms
-
- Animals
- Automation, Laboratory/methods
- Brain/physiopathology*
- Disease Models, Animal
- Electroencephalography*/methods
- Epilepsy/diagnosis
- Epilepsy/physiopathology*
- Larva
- Pattern Recognition, Automated/methods*
- Pentylenetetrazole
- Seizures/diagnosis
- Seizures/physiopathology*
- Sensitivity and Specificity
- Signal Processing, Computer-Assisted
- Support Vector Machine*
- Zebrafish
- PubMed
- 28577986 Full text @ J. Neurosci. Methods
Citation
Hunyadi, B., Siekierska, A., Sourbron, J., Copmans, D., de Witte, P.A.M. (2017) Automated analysis of brain activity for seizure detection in zebrafish models of epilepsy. Journal of Neuroscience Methods. 287:13-24.
Abstract
Background Epilepsy is a chronic neurological condition, with over 30% of cases unresponsive to treatment. Zebrafish larvae show great potential to serve as an animal model of epilepsy in drug discovery. Thanks to their high fecundity and relatively low cost, they are amenable to high-throughput screening. However, the assessment of seizure occurrences in zebrafish larvae remains a bottleneck, as visual analysis is subjective and time-consuming.
New method For the first time, we present an automated algorithm to detect epileptic discharges in single-channel local field potential (LFP) recordings in zebrafish. First, candidate seizure segments are selected based on their energy and length. Afterwards, discriminative features are extracted from each segment. Using a labeled dataset, a support vector machine (SVM) classifier is trained to learn an optimal feature mapping. Finally, this SVM classifier is used to detect seizure segments in new signals.
Results We tested the proposed algorithm both in a chemically-induced seizure model and a genetic epilepsy model. In both cases, the algorithm delivered similar results to visual analysis and found a significant difference in number of seizures between the epileptic and control group.
Comparison with existing methods Direct comparison with multichannel techniques or methods developed for different animal models is not feasible. Nevertheless, a literature review shows that our algorithm outperforms state-of-the-art techniques in terms of accuracy, precision and specificity, while maintaining a reasonable sensitivity.
Conclusion Our seizure detection system is a generic, time-saving and objective method to analyze zebrafish LPF, which can replace visual analysis and facilitate true high-throughput studies.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping