Objective. High-frequency oscillations (HFOs) have emerged as a promising clinical biomarker for presurgical evaluation in childhood epilepsy. HFOs are commonly classified in stereo-encephalography as ripples (80–200 Hz) and fast ripples (200–500 Hz). Ripples are less specific and not so directly associated with epileptogenic activity because of their physiological and pathological origin. The aim of this paper is to distinguish HFOs in the ripple band and to improve the evaluation of the epileptogenic zone (EZ). Approach. This study constitutes a novel modeling approach evaluated in ten patients from Sant Joan de Deu Pediatric Hospital (Barcelona, Spain), with clearly-defined seizure onset zones (SOZ) during presurgical evaluation. A subject-by-subject basis analysis is proposed: a probabilistic Gaussian mixture model (GMM) based on the combination of specific ripple features is applied for estimating physiological and pathological ripple subpopulations. Main Results. Clear pathological and physiological ripples are identified. Features differ considerably among patients showing within-subject variability, suggesting that individual models are more appropriate than a traditional whole-population approach. The difference in rates inside and outside the SOZ for pathological ripples is significantly higher than when considering all the ripples. These significant differences also appear in signal segments without epileptiform activity. Pathological ripple rates show a sharp decline from SOZ to non-SOZ contacts and a gradual decrease with distance. Significance. This novel individual GMM approach improves ripple classification and helps to refine the delineation of the EZ, as well as being appropriate to investigate the interaction of epileptogenic and propagation networks.

客观。高频振荡 (HFO) 已成为儿童癫痫术前评估的有前途的临床生物标志物。HFO 通常在立体脑图中分为波纹 (80-200 Hz) 和快速波纹 (200-500 Hz)。由于其生理和病理起源，涟漪的特异性较低，并且与致癫痫活动没有直接关联。本文的目的是区分波纹带中的 HFO，并改进对致癫痫区 (EZ) 的评估。方法. 这项研究构成了一种新的建模方法，对来自 Sant Joan de Deu 儿科医院（西班牙巴塞罗那）的 10 名患者进行了评估，在术前评估期间具有明确定义的癫痫发作区 (SOZ)。提出了逐个主题的基础分析：将基于特定波纹特征组合的概率高斯混合模型（GMM）应用于估计生理和病理波纹亚群。主要结果. 明确的病理和生理波纹被识别。表现出受试者内变异性的患者的特征差异很大，这表明个体模型比传统的整体方法更合适。SOZ 内部和外部病理波纹的速率差异明显高于考虑所有波纹时。这些显着差异也出现在没有癫痫样活动的信号片段中。病理纹波率显示从 SOZ 到非 SOZ 接触急剧下降，并随着距离逐渐下降。意义。这种新颖的个体 GMM 方法改进了波纹分类并有助于细化 EZ 的描绘，以及适合研究致癫痫网络和传播网络的相互作用。