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Fast Detection and Quantification of Interictal Spikes and Seizures in a Rodent Model of Epilepsy Using an Automated Algorithm
Journal article   Open access   Peer reviewed

Fast Detection and Quantification of Interictal Spikes and Seizures in a Rodent Model of Epilepsy Using an Automated Algorithm

Kyle J Jackson, Shaunik Sharma, Grant Tiarks, Saul Rodriguez and Alexander G. Bassuk
Bio-protocol, Vol.13(6), e4632
03/20/2023
DOI: 10.21769/BioProtoc.4632
PMCID: PMC10031526
PMID: 36968440
url
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031526View
Published (Version of record) Open Access

Abstract

The electroencephalogram (EEG) is a powerful tool for analyzing neural activity in various neurological disorders, both in animals and in humans. This technology has enabled researchers to record the brain’s abrupt changes in electrical activity with high resolution, thus facilitating efforts to understand the brain’s response to internal and external stimuli. The EEG signal acquired from implanted electrodes can be used to precisely study the spiking patterns that occur during abnormal neural discharges. These patterns can be analyzed in conjunction with behavioral observations and serve as an important means for accurate asses sment and quantification of behavioral and electrographic seizures. Numerous algorithms have been developed for the automated quantification of EEG data; however, many of these algorithms were developed with outdated programming languages and require robust computational hardware to run effectively. Additionally, some of these programs require substantial computation time, reducing the relative benefits of automation. Thus, we sought to develop an automated EEG algorithm that was programmed using a familiar programming language (MATLAB), and that could run efficiently without extensive computational demands. This algorithm was developed to quantify interictal spikes and seizures in mice that were subjected to traumatic brain injury. Although the algorithm was designed to be fully automated, it can be operated manually, and all the parameters for EEG activity detection can be easily modified for broad data analysis. Additionally, the algorithm is capable of processing months of lengthy EEG datasets in the order of minutes to hours, reducing both analysis time and errors introduced through manual-based processing.
Algorithm Automated Biology EEG Interictal Spikes MATLAB Methods Seizures

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