[Optimized multi-scale entropy to localize epileptogenic hemisphere of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging]

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Dec 25;38(6):1163-1172. doi: 10.7507/1001-5515.202011048.
[Article in Chinese]

Abstract

Entropy model is widely used in epileptic electroencephalogram (EEG) analysis, but there are few reports on how to objectively select the parameters to compute the entropy model in the analysis of resting-state functional magnetic resonance imaging (rfMRI). Therefore, an optimization algorithm to confirm the parameters in multi-scale entropy (MSE) model was proposed, and the location of epileptogenic hemisphere was taken as an example to test the optimization effect by supervised machine learning. The rfMRI data of 20 temporal lobe epilepsy (TLE) patients with hippocampal sclerosis, positive on structural magnetic resonance imaging, were divided into left and right groups. Then, the parameters in MSE model were optimized by the receiver operating characteristic curves (ROC) and area under ROC curve (AUC) values in sensitivity analysis, and the entropy value of the brain regions with statistically significant difference between the groups were taken as sensitive features to epileptogenic hemisphere lateral. The optimized entropy values of these bio-marker brain areas were considered as feature vectors input into the support vector machine (SVM). Finally, combining optimized MSE model with SVM could accurately distinguish epileptogenic hemisphere in TLE at an average accuracy rate of 95%, which was higher than the current level. The results show that the MSE model parameter optimization algorithm can accurately extract the functional imaging markers sensitive to the epileptogenic hemisphere, and achieve the purpose of objectively selecting the parameters for MSE in rfMRI, which provides the basis for the application of entropy in advanced technology detection.

熵模型广泛应用在癫痫脑电分析中,但其在静息态功能磁共振成像(rfMRI)中尚存在主观选择计算参数的问题。为此,本文提出多尺度熵模型优化算法,联合有监督机器学习检验优化效果。以致痫侧定位为例,将20位海马硬化标记患者分为左、右侧2组,利用敏感性分析指标优化熵模型参数,以组间优化熵值有显著差异的脑区作为对致痫侧敏感的标记,其熵值为特征向量输入支持向量机分类并验证,获得平均准确率达95%的定侧结果,高于目前水平。研究结果显示,熵模型参数优化算法可较为准确地提取对致痫侧敏感的功能影像学标记,达到客观选择癫痫rfMRI熵模型参数的研究目的,为熵应用于先进技术检测提供了依据。.

Keywords: epileptogenic hemisphere; multi-scale entropy; resting-state functional magnetic resonance imaging; support vector machine; temporal lobe epilepsy.

MeSH terms

  • Brain / diagnostic imaging
  • Brain Mapping
  • Entropy
  • Epilepsy, Temporal Lobe* / diagnostic imaging
  • Humans
  • Magnetic Resonance Imaging

Grants and funding

国家自然科学基金资助项目(81871345);河北省自然科学基金资助项目(E2019202019)