Investigation of Stereotyped High-Frequency Oscillations with Computational Intelligence for the Prediction of Seizure Onset Zone in Epilepsy

项目总结 难治性癫痫的神经外科治疗需要准确定位发作起始区(SOZ)。在临床上 实践中，颅内脑电(IEEG)记录在癫痫监测单元(EMU)中的许多天，其中 记录多次癫痫发作以提供定位SOZ的信息。动车组的长时间监控 增加了并发症的风险，可能包括颅内出血和潜在的死亡。最近，很高 IEEG在80~500赫兹的频率振荡(HFO)是一种很有前途的临床生物标志物 治疗癫痫。HFOS被认为具有临床意义，因此可以用于SOZ的定位。然而， 也可以从正常和非癫痫的大脑结构中记录到HFO。如果仅按费率或 频率、病理性HFO与生理性HFO难以区分，限制了其在临床上的应用 癫痫患者的术前计划。在这个方案中，就我们所知，我们显示了一个递归波形 区分病理性HFO和生理性HFO的模式。特别是，我们观察到SOZ 重复产生一组刻板印象的HFO波形，而来自非癫痫区域的HFO 波形形态不规则。基于这些观察，使用基于最近的 稀疏编码和无监督机器学习技术的进展，我们建议检测这些 直接从成人和儿童的连续iEEG数据中提取反复出现的HFO波型 通过将检测到的事件的空间分布与临床结果相关联来测试患者的预后价值 例如SOZ、切除区域和癫痫自由。我们假设病理性HFO的准确检测 简而言之，iEEG记录可以识别SOZ并消除延长动车组监测和 降低相关风险。在这些动机下，在这个项目中，由以下人员组成的跨学科团队 生物医学工程师、癫痫专家和神经外科医生将共同开发和测试新的 在临床记录的大型iEEG数据集中检测刻板印象的HFO及其亚型的计算工具 电极。开发的算法和iEEG数据将与研究社区共享，以促进 可重复的研究，并帮助其他研究小组开发新方法。这项研究的结果将 对于实现我们小组开发在线神经信号处理系统的长期目标至关重要 用于快速准确地识别SOZ，并进行简短的有创记录。

PROJECT SUMMARY Neurosurgical therapy of refractory epilepsy requires accurate localization of seizure onset zone (SOZ). In clinical practice, intracranial EEG (iEEG) is recorded in the epilepsy monitoring unit (EMU) over many days where multiple seizures are recorded to provide information to localize the SOZ. The prolonged monitoring in the EMU adds to the risk of complications and can include intracranial bleeding and potentially death. Recently, high frequency oscillations (HFO) of iEEG between 80 to 500 Hz are highly valued as a promising clinical biomarker for epilepsy. HFOs are believed to be clinically significant, and thus could be used for SOZ localization. However, HFOs can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or frequency, pathological HFOs are indistinguishable from physiological ones, which limit their application in epilepsy pre-surgical planning. In this proposal, to the best of our knowledge, we show of a recurrent waveform pattern that distinguishes pathological HFOs from physiological ones. In particular, we observed that the SOZ generates repeatedly a set of stereotyped HFO waveforms whereas the HFOs from nonepileptic regions were irregular in their waveform morphology. Based on these observations, using computational tools built on recent advances in sparse coding and unsupervised machine learning techniques, we propose to detect these stereotyped recurrent HFO waveform patterns directly from the continuous iEEG data of adult and pediatric patients and test their prognostic value by correlating the spatial distribution of detected events to clinical findings such as SOZ, resection zone and seizure freedom. We hypothesize that accurate detection of pathologic HFOs in brief iEEG recordings can identify the SOZ and eliminate the necessity of prolonged EMU monitoring and reduce the associated risks. With these motivations, in this project an interdisciplinary team composed of biomedical engineers, epileptologists and neurosurgeons will work together to develop and test novel computational tools to detect stereotyped HFOs and its subtypes in large iEEG datasets recorded with clinical electrodes. Developed algorithms and iEEG data will be shared with the research community to contribute to the reproducible research and help other research groups to develop novel methods. The results of this study will be essential for achieving our group's long term goal of developing an online neural signal processing system for the rapid and accurate identification of SOZ with brief invasive recording.