Department of Neurology, Zhengzhou University People's Hospital, Zhengzhou, Henan Province, China.
Department of Rehabilitation, The first Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China.
Epileptic Disord. 2023 Jun;25(3):331-342. doi: 10.1002/epd2.20044. Epub 2023 Apr 28.
To analyze whether the Lempel-Ziv Complexity (LZC) in quantitative electroencephalogram differs between the temporal lobe epilepsy (TLE) patients with or without cognitive impairment (CI) and explore the diagnostic value of LZC for identifying CI in TLE patients.
Twenty-two clinical features and 20-min EEG recordings were collected from 48 TLE patients with CI and 27 cognitively normal (CON) TLE patients. Seventy-six LZC features were calculated for 19 leads in four frequency bands (alpha, beta, delta, and theta). The clinical and LZC features were compared between the two groups. A support vector machine (SVM) was subsequently constructed using the leave-one-out method of cross-validation for LZC features with statistical differences.
Regarding the clinical features, the level of education (p < .001), hippocampal atrophy and sclerosis (p = .029), and depression (p = .037) were statistically different between the two groups. For the LZC features, there were statistically significant differences in the alpha (Fp1, Fz, Cz, Pz, C3, C4, T3, T4, T5, T6, F3, F4, F7, F8, O1, and O2), beta (Fp2), and theta (F7) oscillations. The mean LZC in the alpha band was higher in the TLE-CI group than that in the CON group, and there were no differences in the remaining bands. The SVM model showed 74.51% accuracy, 79.63% sensitivity, 84.30% F1 score, 68.75% specificity, and .85 area under the curve scores.
The LZC in the alpha band might have the potential to be used as a biomarker for the diagnosis of TLE combined with CI. The TLE-CI group, on the other hand, exhibited a higher degree of complexity in alpha oscillations, which were widespread and occurred in all brain regions.
分析颞叶癫痫(TLE)伴或不伴认知障碍(CI)患者定量脑电图的李普希茨复杂度(LZC)是否存在差异,并探讨 LZC 对 TLE 患者 CI 识别的诊断价值。
收集 48 例 TLE 伴 CI 患者和 27 例认知正常(CON)TLE 患者的 22 项临床特征和 20min 脑电图记录。在四个频带(α、β、δ 和θ)的 19 个导程上计算了 76 个 LZC 特征。比较两组之间的临床和 LZC 特征。随后,使用交叉验证的留一法构建了基于具有统计学差异的 LZC 特征的支持向量机(SVM)。
在临床特征方面,两组患者的受教育程度(p<0.001)、海马萎缩和硬化(p=0.029)和抑郁(p=0.037)存在统计学差异。在 LZC 特征方面,α(Fp1、Fz、Cz、Pz、C3、C4、T3、T4、T5、T6、F3、F4、F7、F8、O1 和 O2)、β(Fp2)和θ(F7)振荡存在统计学差异。TLE-CI 组的α 波段 LZC 均值高于 CON 组,其余波段无差异。SVM 模型的准确率为 74.51%,灵敏度为 79.63%,F1 得分为 84.30%,特异性为 68.75%,曲线下面积为.85。
α 波段的 LZC 可能具有作为 TLE 合并 CI 诊断生物标志物的潜力。TLE-CI 组在所有脑区均表现出更广泛的 α 振荡复杂性,其复杂性程度更高。