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

利用计算智能研究刻板高频振荡以预测癫痫发作发作区

• 批准号: 9974350
• 负责人: Nuri Firat Ince
• 金额: $ 45.93万
• 依托单位: UNIVERSITY OF HOUSTON
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9974350
• 关键词: Adult; Algorithms; Area; Biomedical Engineering; Brain; Cerebrum; Cessation of life; Characteristics; Child; Clinical; Code; Communities; Data; Data Analyses; Data Set; Databases; Detection; Electrodes; Electroencephalography; Environment; Epilepsy; Event; Excision; Freedom; Frequencies; Goals; Hemorrhage; High Frequency Oscillation; Hospitals; Hour; Intractable Epilepsy; Investigation; Laboratories; Language; Lesion; Meningoencephalitis; Methods; Modernization; Monitor; Morphology; Motivation; Motor; Motor Cortex; Multicenter Studies; Neocortex; Neurosurgeon; Operative Surgical Procedures; Pathologic; Patients; Pattern; Physiological; Recurrence; Reproducibility; Research; Risk; Scheme; Seizures; Site; Spatial Distribution; Stereotyping; Structure; System; Techniques; Testing; Time; Tissues; Translations; Visual; Work; awake; base; clinical biomarkers; clinical practice; clinically significant; computational intelligence; computerized tools; cost; neurotransmission; novel; pediatric patients; prognostic value; prospective; signal processing; tool; unsupervised learning

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.

项目摘要 难治性癫痫的神经外科治疗需要精确定位癫痫发作区（SOZ）。在临床上 练习，颅内脑电图（IEEG）在癫痫监测单元（EMU）中记录了很多天 记录了多次癫痫发作以提供信息来本地化SOZ。 EMU中的延长监控 增加并发症的风险，包括颅内出血和潜在死亡。最近，高 IEEG的频率振荡（HFO）在80至500 Hz之间被高度重视为有希望的临床生物标志物 用于癫痫。 HFO被认为具有临床意义，因此可以用于SOZ定位。然而， HFO也可以从正常和非癫痫性大脑结构中记录。仅按速率定义或 频率，病理HFO与生理生理学的HFO无法区分，这限制了其在 癫痫病前计划。在这一建议中，据我们所知，我们展示了复发的波形 将病理HFO与生理HFO区分开的模式。特别是，我们观察到Soz 反复生成一组刻板印象的HFO波形，而来自非癫痫区的HFO是 其波形形态不规则。基于这些观察结果，使用基于最新的计算工具 稀疏编码和无监督的机器学习技术的进步，我们建议检测这些 直接从成人和小儿的连续IEEG数据中刻板的复发性HFO波形模式 患者并通过将检测事件的空间分布与临床发现相关联测试其预后价值 例如SOZ，切除区和癫痫发作自由。我们假设对病理HFO的准确检测 简而 降低相关风险。有了这些动机，在这个项目中，一个跨学科团队由 生物医学工程师，癫痫学家和神经外科医生将共同开发和测试新颖 在大型IEEG数据集中检测刻板印象的HFO及其亚型的计算工具记录了临床 电极。开发的算法和IEEG数据将与研究界共享，以促进 可再现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 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 对于短暂的侵入性记录，快速准确地识别SOZ。