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, Greg A. Worrell, Charles L. Howe

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

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

ASJC Scopus subject areas

  • General

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