Institute of Scientific Instruments of the CAS, Brno, Czech Republic.
Department of Neurology, Faculty of Medicine, Brno Epilepsy Center, St. Anne's University Hospital, Member of ERN-EpiCARE, Masaryk University, Brno, Czech Republic.
Epilepsia. 2024 Oct;65(10):2935-2945. doi: 10.1111/epi.18081. Epub 2024 Aug 24.
Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction.
In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test.
The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution.
Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.
有证据表明，通过机器学习模型中多种立体脑电图（SEEG）生物标志物的组合，可以实现癫痫灶（EZ）的最佳间测定位。这些生物标志物通常包括在标准频带中计算的 SEEG 特征，但也包括高频（HF）频带。不幸的是，HF 特征需要额外的精力来记录、存储和处理。在这里，我们研究了这些 HF 特征对 EZ 定位和术后结果预测的附加价值。
我们在 50 名患者中分析了非快速眼动睡眠期间记录的 30 分钟 SEEG，并使用三个不同的特征集测试了逻辑回归模型。第一个模型使用宽带特征（1-500 Hz）；第二个模型使用低频特征（高达 45 Hz）；第三个模型使用 HF 特征（高于 65 Hz）。通过多种指标评估每个模型的 EZ 定位，包括精确召回曲线下面积（AUPRC）和阳性预测值（PPV）。通过 Wilcoxon 符号秩检验和 Cliff's Delta 效应大小检验来检验模型之间的差异。进一步通过 McNemar 检验检验基于 PPV 值的结果预测差异。
随机机会分类器的 AUPRC 评分为 0.098。模型（宽带、低频、高频）的中位 AUPRC 分别为 0.608、0.582 和 0.522，分别正确预测了 38、38 和 33 名患者的结果。三个模型之间的 AUPRC 或任何其他指标均无统计学差异。向模型中添加 HF 特征并没有任何额外的贡献。
低频特征足以正确定位 EZ 并预测结果，考虑 HF 特征时没有额外价值。这一发现允许对特征计算过程进行重大简化，并为在临床常规中常用的较低采样率的 SEEG 记录中使用这些模型开辟了可能性。
