Second Clinical Medical School, Zhejiang Chinese Medical University, Hangzhou, China.
Department of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China.
Sci Rep. 2024 Mar 14;14(1):6198. doi: 10.1038/s41598-024-56827-3.
Accurately identification of the seizure onset zone (SOZ) is pivotal for successful surgery in patients with medically refractory epilepsy. The purpose of this study is to improve the performance of model predicting the epilepsy surgery outcomes using genetic neural network (GNN) model based on a hybrid intracranial electroencephalography (iEEG) marker. We extracted 21 SOZ related markers based on iEEG data from 79 epilepsy patients. The least absolute shrinkage and selection operator (LASSO) regression was employed to integrated seven markers, selected after testing in pairs with all 21 biomarkers and 7 machine learning models, into a hybrid marker. Based on the hybrid marker, we devised a GNN model and compared its predictive performance for surgical outcomes with six other mainstream machine-learning models. Compared to the mainstream models, underpinning the GNN with the hybrid iEEG marker resulted in a better prediction of surgical outcomes, showing a significant increase of the prediction accuracy from approximately 87% to 94.3% (P = 0.0412). This study suggests that the hybrid iEEG marker can improve the performance of model predicting the epilepsy surgical outcomes, and validates the effectiveness of the GNN in characterizing and analyzing complex relationships between clinical data variables.
准确识别癫痫发作起始区（SOZ）对于药物难治性癫痫患者的成功手术至关重要。本研究旨在通过基于混合颅内脑电图（iEEG）标志物的遗传神经网络（GNN）模型来提高模型预测癫痫手术结果的性能。我们从 79 名癫痫患者的 iEEG 数据中提取了 21 个与 SOZ 相关的标志物。最小绝对收缩和选择算子（LASSO）回归用于将经过与所有 21 个生物标志物和 7 个机器学习模型成对测试后选择的七个标志物集成到混合标志物中。基于混合标志物，我们设计了一个 GNN 模型，并将其用于预测手术结果的性能与其他六个主流机器学习模型进行了比较。与主流模型相比，基于混合 iEEG 标志物的 GNN 对手术结果的预测更为准确，预测准确性从约 87%显著提高到 94.3%（P=0.0412）。这项研究表明，混合 iEEG 标志物可以提高模型预测癫痫手术结果的性能，并验证了 GNN 在描述和分析临床数据变量之间复杂关系方面的有效性。
