Trainable Early Warning System for Epileptic Seizures

可训练的癫痫发作早期预警系统

• 批准号: 6993754
• 负责人: GEORGIY R MINASYAN
• 金额: $ 30.61万
• 依托单位: ASTRO-MED, INC.
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-03-01 至 2006-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6993754
• 关键词: artificial intelligence; bioimaging /biomedical imaging; brain disorder diagnosis; brain electrical activity; brain imaging /visualization /scanning; brain mapping; computer assisted diagnosis; computer assisted medical decision making; computer program /software; computer system design /evaluation; diagnosis design /evaluation; diagnosis quality /standard; early diagnosis; electroencephalography; epilepsy; human subject; neuropsychology; noninvasive diagnosis; patient monitoring device; radiotracer; single photon emission computed tomography

DESCRIPTION (provided by applicant): Individuals with epilepsy, their families and the professionals that treat them have long felt the need for reliable warning of an impending epileptic seizure. In Phase I, we developed a unique, multi-parameter, individualized, trainable seizure predetection system which extracts spectral, wavelet and time domain features and uses recurrent neural networks to analyze many hours of multichannel EEG and predetect seizure onsets with a high level of accuracy and reliability. The underlying analytical software for this system was tested on large database of scalp EEG recordings from five epilepsy centers and contained over 373 hours of recordings from 17 patients containing 50 seizures. This is a large database compared to those reported by most investigators in this field. Phase I results were very accurate and reliable (sensitivity: 100%, false positive rate 0.02/hr and detection times that occurred an average of 9.1 seconds, before seizure onset). This is entirely acceptable for our primary target application, of warning of a seizure onset to enable timely diagnostic injection to locate the brain region where the seizure starts using SPECT imaging. In Phase II we will refine the detection process, validate performance in a large data set, implement the software in C++ modules, and test the commercial prototype in a clinical setting. These steps will result in a company supported Phase III product development, integration into existing EEG recording systems, beta testing and commercialization. While Phase 2 will concentrate on the SPECT application using scalp electrodes, the scalp EEG techniques developed will be applicable without change for general use in an epilepsy monitoring facility to alert staff of impending seizures and allow them to attend to patient safety in a timely manner. Predetection will also facilitate neuropsychological or other testing in the epilepsy monitoring environment. With additional work, the techniques developed can be applied to ambulatory devices for seizure alert, or for episodic vagal nerve stimulation to block seizures.

描述（由申请人提供）：患有癫痫病的人，他们的家人和对待他们的专业人员长期以来都需要对即将发生的癫痫发作的可靠警告。在第一阶段，我们开发了一个独特的，多参数，个性化的，可训练的癫痫发作预测系统，该系统提取光谱，小波和时域特征，并使用复发性神经网络来分析许多小时的多通道EEG和预测癫痫发作，并具有高度准确性和可靠性和可靠性。该系统的基本分析软件在来自五个癫痫中心的头皮脑电图记录数据库上进行了测试，并包含17例含有50次癫痫发作的患者的373个小时的记录。与该领域的大多数研究人员报告的数据库相比，这是一个很大的数据库。第一阶段的结果非常准确且可靠（灵敏度：100％，假阳性率为0.02/hr，检测时间平均为9.1秒，在癫痫发作之前）。这对于我们的主要目标应用是完全可以接受的，警告发作发作以及时诊断注射以定位癫痫发作开始使用SPECT成像开始的大脑区域。在第二阶段，我们将完善检测过程，验证大型数据集中的性能，在C ++模块中实现软件，并在临床环境中测试商业原型。这些步骤将导致公司支持的III期产品开发，集成到现有的EEG记录系统中，Beta测试和商业化。虽然第2阶段将使用头皮电极专注于SPECT应用，但开发的头皮脑电图技术将不适用，而无需更改癫痫监测设施，以提醒员工即将癫痫发作，并允许他们及时参加患者安全。预测还将促进癫痫监测环境中的神经心理学或其他测试。通过额外的工作，开发的技术可以应用于癫痫发作的卧床设备，也可以用于发作的迷走神经刺激以阻止癫痫发作。