Automated identification of multiple seizure-related and interictal epileptiform event types in the EEG of mice

Rachel A. Bergstrom, Jee Hyun Choi, Armando Manduca, Hee Sup Shin, Gregory Alan Worrell, Charles L Howe

Research output: Contribution to journalArticle

34 Citations (Scopus)

Abstract

Visual scoring of murine EEG signals is time-consuming and subject to low inter-observer reproducibility. The Racine scale for behavioral seizure severity does not provide information about interictal or sub-clinical epileptiform activity. An automated algorithm for murine EEG analysis was developed using total signal variation and wavelet decomposition to identify spike, seizure, and other abnormal signal types in single-channel EEG collected from kainic acid-treated mice. The algorithm was validated on multi-channel EEG collected from γ-butyrolacetone-treated mice experiencing absence seizures. The algorithm identified epileptiform activity with high fidelity compared to visual scoring, correctly classifying spikes and seizures with 99% accuracy and 91% precision. The algorithm correctly identifed a spike-wave discharge focus in an absence-type seizure recorded by 36 cortical electrodes. The algorithm provides a reliable and automated method for quantification of multiple classes of epileptiform activity within the murine EEG and is tunable to a variety of event types and seizure categories.

Original languageEnglish (US)
Article number1483
JournalScientific Reports
Volume3
DOIs
StatePublished - 2013

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Electroencephalography
Seizures
Absence Epilepsy
Kainic Acid
Electrodes

ASJC Scopus subject areas

  • General

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Automated identification of multiple seizure-related and interictal epileptiform event types in the EEG of mice. / Bergstrom, Rachel A.; Choi, Jee Hyun; Manduca, Armando; Shin, Hee Sup; Worrell, Gregory Alan; Howe, Charles L.

In: Scientific Reports, Vol. 3, 1483, 2013.

Research output: Contribution to journalArticle

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