Borbála Hunyadi1, Aleksandra Siekierska2, Jo Sourbron2, Daniëlle Copmans2, Peter A M de Witte2. 1. STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium; imec, Leuven, Belgium. Electronic address: borbala.hunyadi@esat.kuleuven.be. 2. Laboratory for Molecular Biodiscovery, KU Leuven, Campus Gasthuisberg, Herestraat 49, O&N II, 3000 Leuven, Belgium.
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.
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.
Authors: Douglas G Howe; Judith A Blake; Yvonne M Bradford; Carol J Bult; Brian R Calvi; Stacia R Engel; James A Kadin; Thomas C Kaufman; Ranjana Kishore; Stanley J F Laulederkind; Suzanna E Lewis; Sierra A T Moxon; Joel E Richardson; Cynthia Smith Journal: Lab Anim (NY) Date: 2018-09-17 Impact factor: 12.625
Authors: Ricardo Fuentes; Joaquín Letelier; Benjamin Tajer; Leonardo E Valdivia; Mary C Mullins Journal: Mech Dev Date: 2018-08-18 Impact factor: 1.882
Authors: Mirja Tamara Prentzell; Ulrike Rehbein; Marti Cadena Sandoval; Ann-Sofie De Meulemeester; Ralf Baumeister; Laura Brohée; Bianca Berdel; Mathias Bockwoldt; Bernadette Carroll; Suvagata Roy Chowdhury; Andreas von Deimling; Constantinos Demetriades; Gianluca Figlia; Mariana Eca Guimaraes de Araujo; Alexander M Heberle; Ines Heiland; Birgit Holzwarth; Lukas A Huber; Jacek Jaworski; Magdalena Kedra; Katharina Kern; Andrii Kopach; Viktor I Korolchuk; Ineke van 't Land-Kuper; Matylda Macias; Mark Nellist; Wilhelm Palm; Stefan Pusch; Jose Miguel Ramos Pittol; Michèle Reil; Anja Reintjes; Friederike Reuter; Julian R Sampson; Chloë Scheldeman; Aleksandra Siekierska; Eduard Stefan; Aurelio A Teleman; Laura E Thomas; Omar Torres-Quesada; Saskia Trump; Hannah D West; Peter de Witte; Sandra Woltering; Teodor E Yordanov; Justyna Zmorzynska; Christiane A Opitz; Kathrin Thedieck Journal: Cell Date: 2021-01-25 Impact factor: 41.582
Authors: Aliesha Griffin; Colleen Carpenter; Jing Liu; Rosalia Paterno; Brian Grone; Kyla Hamling; Maia Moog; Matthew T Dinday; Francisco Figueroa; Mana Anvar; Chinwendu Ononuju; Tony Qu; Scott C Baraban Journal: Commun Biol Date: 2021-06-03
Authors: Ewelina Kozioł; Krzysztof Jóźwiak; Barbara Budzyńska; Peter A M de Witte; Daniëlle Copmans; Krystyna Skalicka-Woźniak Journal: Int J Mol Sci Date: 2021-10-22 Impact factor: 5.923
Authors: Muhammad Faiz Johan Arief; Brandon Kar Meng Choo; Jia Ling Yap; Yatinesh Kumari; Mohd Farooq Shaikh Journal: Front Pharmacol Date: 2018-06-27 Impact factor: 5.810
Authors: Olga Cozzolino; Federico Sicca; Emanuele Paoli; Francesco Trovato; Filippo M Santorelli; Gian Michele Ratto; Maria Marchese Journal: Cells Date: 2020-03-21 Impact factor: 6.600