Neurobiology of Disease PhD Program, Mayo Graduate School, Mayo Clinic, Rochester, MN 55905, USA.
Sci Rep. 2013;3:1483. doi: 10.1038/srep01483.
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.
对小鼠 EEG 信号进行视觉评分既耗时又容易导致观察者间的可重复性低。行为性癫痫发作严重程度的 Racine 量表并不能提供关于发作间期或亚临床癫痫样活动的信息。本文开发了一种用于小鼠 EEG 分析的自动化算法，该算法使用总信号变化和小波分解来识别在从海人酸处理的小鼠中采集的单通道 EEG 中的尖峰、癫痫发作和其他异常信号类型。该算法在从γ-丁内酯处理的经历失神发作的小鼠中采集的多通道 EEG 上进行了验证。与视觉评分相比，该算法能够高度准确地识别癫痫样活动，对尖峰和癫痫发作的正确分类准确率为 99%，精密度为 91%。该算法正确识别了由 36 个皮质电极记录的失神样发作中的棘波-慢波放电灶。该算法为定量分析小鼠 EEG 中的多种癫痫样活动提供了一种可靠且自动化的方法，并且可以针对各种事件类型和癫痫发作类别进行调整。
