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.

癫痫是一种神经系统疾病，仅次于中风，影响了世界人口的1％。该项目的总体目的是开发用于脑电图（EEC）监测系统的商业癫痫发作警告和预测（ESWP）软件包。由于其创新以及在临床和研究环境中对这种系统的直接需求，其在一个临床中心的实施和测试将是第一阶段的重点，并且它在几个临床中心和设置中的验证将是 在第二阶段中解决。所提出的ESWP软件将使用Stlmax算法进行癫痫发作预测（Lasemidis等，2003），该预测是基于对脑电图的非线性动力学分析而开发的，并实现并使用其他潜在的癫痫发作预测算法。 Stlmax最初对5例持续时间进行了持续时间为0.76至5.84天的连续离线脑电图记录，持续时间为0.76至5.84天，STLMAX的持久性颞叶癫痫患者的持续时间为82％，在癫痫发作的敏感性为82％，虚假预测率为0.16，平均为71.7分钟的警告。 预测癫痫发作。该项目的第一阶段将实现以下总体目标：（1）使用对现有的商业脑电图仪器之一获取的脑电图数据实现的测试数据分析算法，在发作开始之前，使用一种现有的商业脑电图仪器进行了实时和在线分析这些数据，以发出癫痫发作警告30-90分钟。 （2）测试和完善与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）使用癫痫发作警告来改善诊断，预后和癫痫患者的治疗。该项目的前瞻性阶段将实现以下目的：（1）改进并扩展了ESWP软件包，以与其他现有的商业EEG监测系统一起使用商业化和许可到EEG系统制造商，（2）进行广泛的临床研究以测试Mutiple Clinical Centers中ESWP软件的性能。拟议系统的成功实施将找到几种临床和研究应用程序，以治疗 癫痫，例如通过在癫痫发作前很长一段时间内通知现有护理人员，以提高患者的安全性，时间剂量的抗惊厥药（药物或电磁刺激）在癫痫病用局部局部定位的放射学研究中，在放射性诊断过程中的控制或时间症状诊断过程中提高其功效。