Seizure Prediction System--Clinical Tool for Epilepsy

癫痫发作预测系统--癫痫的临床工具

• 批准号: 6991841
• 负责人: VICTOR BAIDOON
• 金额: $ 10万
• 依托单位: EEGSOFT, INC.
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2006-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6991841
• 关键词: brain electrical activity; clinical biomedical equipment; clinical research; computational biology; computer assisted diagnosis; computer assisted medical decision making; computer program /software; computer system design /evaluation; diagnosis design /evaluation; disease /disorder prevention /control; electrodes; electroencephalography; epilepsy; human data; mathematical model; patient care management; prognosis; sign /symptom

Epilepsy is a neurological disorder, second only to stroke, that affects 1% of the world's population. The overall aim of this project is to develop a commercial epileptic seizure warning and prediction (ESWP) software package for use in electroencephalographic (EEC) monitoring systems from multiple manufacturers. Because of its innovation and the immediate need for such a system in a clinical and research setting, its implementation and testing in one clinical center will be the focus of Phase I, and its validation in several clinical centers and settings will be addressed in Phase II. The proposed ESWP software will use the STLmax algorithm for seizure prediction (lasemidis et al., 2003), developed on the basis of a nonlinear dynamical analysis of EEG, as well as implement and use other potential seizure prediction algorithms. Initially tested on continuous off-line EEG recordings of 0.76 to 5.84 days in duration, from 5 patients with refractory temporal lobe epilepsy, STLmax resulted into 82% sensitivity of seizure prediction, a false prediction rate of 0.16 per hour, and a warning on average 71.7 minutes prior to a predicted seizure. The following overall goals will be accomplished in Phase I of this project: (1) Test data analysis algorithms implemented in ESWP system using access to EEG data acquired with one of the existing commercial EEG instruments, analyze these data in real time and on-line to issue epileptic seizure warnings 30-90 minutes prior to a seizure onset. (2) Test and refine a clinically relevant graphical user interface (GUI), which will control ESWP and allow the medical personnel (technicians, nurses, physicians) to use the seizure warnings to improve the diagnosis, prognosis, and treatment of epileptic patients. The prospective Phase II of this project will accomplish the following aims: (1) Refine and extend the ESWP software package to work with other major existing commercial EEG monitoring systems en route to commercialization and licensing to EEG system manufacturers, (2) Perform extensive clinical studies to test the performance of the ESWP software in mutiple clinical centers. Successful implementation of the proposed system would find several clinical and research applications for the treatment of epilepsy, e.g. improve patient safety by alerting the existing nursing staff long prior to a seizure, time the doses of anticonvulsants (drugs or electromagnetic stimuli) to improve their efficacy in the control of seizures or time ictal diagnostic procedures in radiological studies for epileptogenic foci localization.

癫痫是一种仅次于中风的神经系统疾病，影响着世界 1% 的人口。该项目的总体目标是开发商业癫痫发作警告和预测 (ESWP) 软件包，用于多个制造商的脑电图 (EEC) 监测系统。由于其创新性以及临床和研究环境中对此类系统的迫切需求，其在一个临床中心的实施和测试将成为第一阶段的重点，并且其在多个临床中心和环境中的验证将是 在第二阶段解决。所提出的 ESWP 软件将使用 STLmax 算法进行癫痫发作预测（lasemidis 等，2003），该算法是在脑电图非线性动态分析的基础上开发的，以及实现和使用其他潜在的癫痫发作预测算法。最初对 5 名难治性颞叶癫痫患者进行了 0.76 至 5.84 天的连续离线脑电图记录测试，STLmax 的癫痫预测灵敏度为 82%，每小时错误预测率为 0.16 次，平均发出警告比赛前 71.7 分钟 预测的癫痫发作。该项目第一阶段将实现以下总体目标：（1）使用现有商用脑电图仪器获取的脑电图数据来测试 ESWP 系统中实现的数据分析算法，实时在线分析这些数据在癫痫发作前 30-90 分钟发出癫痫发作警告。 （2）测试和完善临床相关的图形用户界面（GUI），该界面将控制ESWP并允许医务人员（技术人员、护士、医生）使用癫痫警告来改善癫痫患者的诊断、预后和治疗。该项目的预期第二阶段将实现以下目标：(1) 完善和扩展 ESWP 软件包，以便与其他现有的主要商业脑电图监测系统配合使用，并获得脑电图系统制造商的商业化和许可，(2) 进行广泛的临床研究在多个临床中心测试 ESWP 软件的性能。所提出的系统的成功实施将为治疗以下疾病找到多种临床和研究应用： 癫痫症，例如通过在癫痫发作前很久向现有护理人员发出警报，确定抗惊厥药（药物或电磁刺激）的剂量以提高其控制癫痫发作的功效，或在放射学研究中确定癫痫病灶定位的发作期诊断程序，从而提高患者的安全性。