Automatic EEG/MEG-based Epileptogenic Zone Detection

基于 EEG/MEG 的自动致痫区检测

• 批准号: 7127612
• 负责人: Richard E. Greenblatt
• 金额: $ 36.74万
• 依托单位: SOURCE SIGNAL IMAGING, INC.
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-12-22 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7127612
• 关键词: bioimaging /biomedical imaging; brain electrical activity; computer assisted diagnosis; computer assisted patient care; computer simulation; computer system design /evaluation; electroencephalography; epilepsy; human data; magnetic resonance imaging; magnetoencephalography; statistics /biometry

DESCRIPTION (provided by applicant): The epilepsies are a family of disorders of brain dynamics. Patients are susceptible to seizures (changes in sensation, awareness, or behavior caused by brief electrical disturbances in the brain). Often, the electrical disturbances originate from an "epileptogenic zone" in part of the brain, from which the disturbances are propagated to other parts of the brain. Epilepsy affects approximately 2.5 million persons in the United States, over 50 million persons worldwide, and 150,000 to 200,000 new cases occur annually in the U.S. Although most epilepsy patients can control their seizures with the use of antiepileptic drugs, about 20% of patients cannot bring their seizures under control using drug therapy. Many patients with pharmacologically intractable seizures can eliminate their disability largely or completely by neurosurgical intervention, which typically involves resection of tissue in the epileptogenic zone to prevent the spread of electrical disturbances. Candidates for epilepsy surgery are generally evaluated with scalp electroencephalography (EEC) telemetry, and, increasingly, with magnetoencephalography (MEG). We propose to develop computer software that will aid clinicians in the diagnosis and treatment of medically intractable epilepsy, using data obtained from structural magnetic resonance imaging (sMRI), MEG, and EEG. Our goal is to develop and validate improved methods for the non-invasive localization of the epileptogenic zone through the automated analysis of spike-like electrical activity recorded in the interval between seizures (interictal spikes). During Phase I, we improved the sensitivity, specificity, and computational efficiency of our automatic interictal spike identification and classification software. The software was evaluated, using MEG and sMRI data obtained from epilepsy surgery patients. During Phase II, we will add additional features for spike propagation analysis, extend the validation to EEG data, and incorporate the resulting algorithms into our commercial EMSE Suite software.

描述（由申请人提供）：癫痫病是大脑动态疾病的家族。患者容易受到癫痫发作的影响（感觉，意识的变化或由大脑短暂的电动障碍引起的）。通常，电干扰起源于大脑一部分的“癫痫发作区”，从中扰动传播到大脑的其他部位。癫痫会影响美国大约250万人，全球超过5000万人，每年在美国发生150,000至200,000例新病例，尽管大多数癫痫患者可以通过使用抗癫痫药物来控制其癫痫发作，约有20％的患者无法通过使用治疗治疗来控制其癫痫发作。许多具有药理学性疾病的患者可以通过神经外科干预大部分或完全消除其残疾，这通常涉及在癫痫发作区切除组织以防止电动障碍的扩散。通常通过头皮脑电图（EEC）遥测来评估癫痫手术的候选物，并越来越多地通过磁脑电图（MEG）评估。我们建议使用从结构磁共振成像（SMRI），MEG和EEG获得的数据来开发计算机软件，以帮助临床医生诊断和治疗医学上棘手的癫痫。我们的目标是通过对癫痫发作间隔中记录的尖峰样电活动的自动分析（间隔尖峰）的自动分析，开发和验证改进的方法，以通过对癫痫发作区域的非侵入性定位。在第一阶段，我们提高了自动间隔尖峰识别和分类软件的灵敏度，特异性和计算效率。使用从癫痫手术患者获得的MEG和SMRI数据评估了该软件。在第二阶段，我们将添加用于尖峰传播分析的其他功能，将验证扩展到脑电图数据，并将所得算法纳入我们的商业EMSE Suite Suite软件。