Automated identification of multiple seizure-related and interictal epileptiform event types in the EEG of mice
- Automated identification of multiple seizure-related and interictal epileptiform event types in the EEG of mice
- Bergstrom RA; 최지현; Manduca A; 신희섭; Worrell GA; Howe CL
- Issue Date
- Scientific Reports
- VOL 3, 1483-1-1483-8
- 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 c-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.
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