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.
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DOI:
10.1038/srep01483
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发表时间:
2013
影响因子:
4.6
通讯作者:
Howe, Charles L.
中科院分区:
文献类型:
--
作者:
Bergstrom, Rachel A.;Choi, Jee Hyun;Manduca, Armando;Shin, Hee-Sup;Worrell, Greg A.;Howe, Charles L.
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.
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