Linear time-varying model characterizes invasive EEG signals generated from complex epileptic networks.
Linear time-varying model characterizes invasive EEG signals generated from complex epileptic networks.
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DOI：
10.1109/embc.2017.8037439
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发表时间：
2017-07
期刊：
影响因子：
--
通讯作者：
Sarma SV
中科院分区：
文献类型：
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作者：
Li A;Gunnarsdottir KM;Inati S;Zaghloul K;Gale J;Bulacio J;Martinez-Gonzalez J;Sarma SV
皮质电图（ECoG）和立体定向脑电图（SEEG）是研究行为和神经疾病（如癫痫）的神经机制的常用工具。特别是，临床医生对识别开始癫痫发作的大脑区域感兴趣，即，癫痫区（EZ）从这种侵入性的记录。目前，他们目视检查来自每个电极的信号以定位异常活动，并且没有得到预测模型的通知，这些模型可以表征这些记录并可能提高定位EZ的准确性。在本文中，我们测试一个简单的线性时变（LTV）模型是否足以表征ECoG和SEEG活动。具体来说，我们构建线性时不变模型在连续的时间窗口之前，期间和之后的癫痫发作事件创建一个LTV模型从收集的数据在一个ECoG和一个SEEG患者。我们发现，这些LTV模型准确地重建ECoG和SEEG时间序列测量表明，这些LTV模型可用于EZ本地化。
Electrocorticography (ECoG) and stereotactic electroencephalography (SEEG) are popular tools for studying neural mechanisms governing behavior and neural disorders, such as epilepsy. In particular, clinicians are interested in identifying brain regions that start seizures, i.e., the epileptogenic zone (EZ) from such invasive recordings. Currently, they visually inspect signals from each electrode to locate abnormal activity, and are not informed by predictive models that can characterize such recordings and potentially increase accuracy in localizing the EZ. In this paper, we test whether a simple linear time varying (LTV) model is sufficient to characterize both ECoG and SEEG activity. Specifically, we construct linear time invariant models in consecutive time windows before, during and after seizure events creating an LTV model from data collected in one ECoG and one SEEG patient. We find that these LTV models accurately reconstruct both ECoG and SEEG time series measured suggesting that these LTV models can be used for EZ localization.