Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN, USA.
Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN, USA.
Seizure. 2017 Oct;51:35-42. doi: 10.1016/j.seizure.2017.07.010. Epub 2017 Jul 25.
Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (ECoG) recordings to serve as biomarkers to identify the seizure onset zone (SOZ).
We computed the PLV between the phase of the amplitude of high gamma activity (80-150Hz) and the phase of lower frequency rhythms (4-30Hz) from ECoG recordings obtained from 10 patients with epilepsy (21 seizures). We extracted five features from the PLV and used a machine learning approach based on logistic regression to build a model that classifies electrodes as SOZ or non-SOZ.
More than 96% of electrodes identified as the SOZ by our algorithm were within the resected area in six seizure-free patients. In four non-seizure-free patients, more than 31% of the identified SOZ electrodes by our algorithm were outside the resected area. In addition, we observed that the seizure outcome in non-seizure-free patients correlated with the number of non-resected SOZ electrodes identified by our algorithm.
This machine learning approach, based on features extracted from the PLV, effectively identified electrodes within the SOZ. The approach has the potential to assist clinicians in surgical decision-making when pre-surgical intracranial recordings are utilized.
利用一种基于相位锁定值（PLV）的新方法，我们研究了从脑电描记术（ECoG）记录中提取的特征是否可以作为生物标志物，以识别癫痫发作起始区（SOZ）。
我们从 10 名癫痫患者（21 次癫痫发作）的 ECoG 记录中计算了高伽马活动（80-150Hz）幅度相位和低频节律（4-30Hz）相位之间的 PLV。我们从 PLV 中提取了五个特征，并使用基于逻辑回归的机器学习方法构建了一个模型，该模型可以将电极分类为 SOZ 或非 SOZ。
在 6 名无癫痫发作的患者中，我们的算法识别的 SOZ 电极中，超过 96%位于切除区域内。在 4 名非无癫痫发作的患者中，我们的算法识别的 SOZ 电极中，超过 31%位于切除区域外。此外，我们观察到非无癫痫发作患者的癫痫发作结果与我们的算法识别的非切除 SOZ 电极数量相关。
基于从 PLV 中提取的特征的这种机器学习方法有效地识别了 SOZ 内的电极。该方法在利用术前颅内记录进行手术决策时，有可能辅助临床医生。
