A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model

Background: Animal models of absence epilepsy are widely used in childhood absence epilepsy studies. Absence seizures appear in the brain’s electrical activity as a specific spike wave discharge (SWD) pattern. Reviewing long-term brain electrical activity is time-consuming and automatic methods are...

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Autores principales:	Saleh Lashkari, Ali Moghimi, Hamid Reza Kobravi, Mohamad Amin Younessi Heravi
Formato:	article
Lenguaje:	EN
Publicado:	Shahid Beheshti University of Medical Sciences 2021
Materias:	absence epilepsy eeg wag/rij animal model phase space geometrical features Medicine R
Acceso en línea:	https://doaj.org/article/f6dfe4a0044d483bb4513cbb68fdc3ac
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spelling	oai:doaj.org-article:f6dfe4a0044d483bb4513cbb68fdc3ac2021-11-16T10:51:30ZA Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model2383-18712383-209610.34172/icnj.2021.36https://doaj.org/article/f6dfe4a0044d483bb4513cbb68fdc3ac2021-10-01T00:00:00Zhttps://journals.sbmu.ac.ir/neuroscience/article/view/35145/27938https://doaj.org/toc/2383-1871https://doaj.org/toc/2383-2096Background: Animal models of absence epilepsy are widely used in childhood absence epilepsy studies. Absence seizures appear in the brain’s electrical activity as a specific spike wave discharge (SWD) pattern. Reviewing long-term brain electrical activity is time-consuming and automatic methods are necessary. On the other hand, nonlinear techniques such as phase space are effective in brain electrical activity analysis. In this study, we present a novel SWD-detection framework based on the geometrical characteristics of the phase space. Methods: The method consists of the following steps: (1) Rat stereotaxic surgery and cortical electrode implantation, (2) Long-term brain electrical activity recording, (3) Phase space reconstruction, (4) Extracting geometrical features such as volume, occupied space, and curvature of brain signal trajectories, and (5) Detecting SDWs based on the thresholding method. We evaluated the approach with the accuracy of the SWDs detection method. Results: It has been demonstrated that the features change significantly in transition from a normal state to epileptic seizures. The proposed approach detected SWDs with 98% accuracy. Conclusion: The result supports that nonlinear approaches can identify the dynamics of brain electrical activity signals.Saleh LashkariAli MoghimiHamid Reza KobraviMohamad Amin Younessi HeraviShahid Beheshti University of Medical Sciencesarticleabsence epilepsyeegwag/rijanimal modelphase spacegeometrical featuresMedicineRENInternational Clinical Neuroscience Journal, Vol 8, Iss 4, Pp 180-187 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	absence epilepsy eeg wag/rij animal model phase space geometrical features Medicine R
spellingShingle	absence epilepsy eeg wag/rij animal model phase space geometrical features Medicine R Saleh Lashkari Ali Moghimi Hamid Reza Kobravi Mohamad Amin Younessi Heravi A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model
description	Background: Animal models of absence epilepsy are widely used in childhood absence epilepsy studies. Absence seizures appear in the brain’s electrical activity as a specific spike wave discharge (SWD) pattern. Reviewing long-term brain electrical activity is time-consuming and automatic methods are necessary. On the other hand, nonlinear techniques such as phase space are effective in brain electrical activity analysis. In this study, we present a novel SWD-detection framework based on the geometrical characteristics of the phase space. Methods: The method consists of the following steps: (1) Rat stereotaxic surgery and cortical electrode implantation, (2) Long-term brain electrical activity recording, (3) Phase space reconstruction, (4) Extracting geometrical features such as volume, occupied space, and curvature of brain signal trajectories, and (5) Detecting SDWs based on the thresholding method. We evaluated the approach with the accuracy of the SWDs detection method. Results: It has been demonstrated that the features change significantly in transition from a normal state to epileptic seizures. The proposed approach detected SWDs with 98% accuracy. Conclusion: The result supports that nonlinear approaches can identify the dynamics of brain electrical activity signals.
format	article
author	Saleh Lashkari Ali Moghimi Hamid Reza Kobravi Mohamad Amin Younessi Heravi
author_facet	Saleh Lashkari Ali Moghimi Hamid Reza Kobravi Mohamad Amin Younessi Heravi
author_sort	Saleh Lashkari
title	A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model
title_short	A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model
title_full	A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model
title_fullStr	A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model
title_full_unstemmed	A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model
title_sort	novel spike-wave discharge detection framework based on the morphological characteristics of brain electrical activity phase space in an animal model
publisher	Shahid Beheshti University of Medical Sciences
publishDate	2021
url	https://doaj.org/article/f6dfe4a0044d483bb4513cbb68fdc3ac
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A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model

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