Bergstrom, Rachel A. Choi, Jee Hyun Manduca, Armando Shin, Hee-Sup Worrell, Greg A. Howe, Charles L. 2024-01-20T12:34:13Z 2024-01-20T12:34:13Z 2021-09-01 2013-03-21 2045-2322 https://pubs.kist.re.kr/handle/201004/128235 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 gamma-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. English NATURE PUBLISHING GROUP TEMPORAL-LOBE EPILEPSY NEURAL-NETWORK SPIKE ACTIVITY LINE LENGTH KAINIC ACID ONSET ELECTROENCEPHALOGRAM PREDICTION DYNAMICS KAINATE Automated identification of multiple seizure-related and interictal epileptiform event types in the EEG of mice Article 10.1038/srep01483 1 SCIENTIFIC REPORTS, v.3 SCIENTIFIC REPORTS 3 scie scopus 000316539800001 2-s2.0-84875789568 Multidisciplinary Sciences Science & Technology - Other Topics Article TEMPORAL-LOBE EPILEPSY NEURAL-NETWORK SPIKE ACTIVITY LINE LENGTH KAINIC ACID ONSET ELECTROENCEPHALOGRAM PREDICTION DYNAMICS KAINATE