Epilepsy is one of the most common neurological disorders, and there exists a subset of patients with refractory epilepsy that require surgical removal of the epileptogenic foci (EF) area. Studies have shown that high-frequency oscillations (HFOs) in epileptic electroencephalogram signals can be used as an essential biomarker for locating EF. This paper proposes a new method for rapid localization of EF based on the automatic detection of HFOs by waveform feature templates (WFTs). First, the initial screening of HFOs based on Hilbert transform and subsequent rescreening with short-time energy and short-time Fourier transform is performed, and the two screening results are used as the template data set of HFOs. Then, a coarse-grained and fine-grained screening method for detecting HFOs using autocorrelation coefficients and interrelation coefficients as WFT detectors, respectively. Compared with the Hilbert transform detector and other HFOs detector methods proposed at abroad in recent years, the experimental simulations showed that the automatic detector based on WFT could detect HFOs more rapidly, accurately, and efficiently. Our proposed WFT detector has the advantages of high specificity, high sensitivity, and high accuracy in locating EF and has a high clinical utility.

中文翻译：

---

基于波形特征模板的高频振荡自动检测致痫灶定位

癫痫是最常见的神经系统疾病之一，存在一部分难治性癫痫患者需要手术切除致癫痫灶 (EF) 区域。研究表明，癫痫脑电图信号中的高频振荡 (HFO) 可作为定位 EF 的重要生物标志物。本文提出了一种基于波形特征模板 (WFT) 自动检测 HFO 的 EF 快速定位新方法。首先，基于Hilbert变换对HFOs进行初筛，随后采用短时能量和短时傅里叶变换进行复筛，将两次筛选结果作为HFOs的模板数据集。然后，一种用于检测 HFO 的粗粒度和细粒度筛选方法，分别使用自相关系数和互相关系数作为 WFT 检测器。与近年来国外提出的希尔伯特变换检测器等HFOs检测方法相比，实验仿真表明，基于WFT的自动检测器能够更快速、准确、高效地检测HFOs。我们提出的 WFT 检测器在定位 EF 方面具有高特异性、高灵敏度和高精度的优点，具有很高的临床实用性。