NEUROMAGNETOMETER COMPUTER SYSTEM

NINCDS医学神经科，生物医学工程学 DRS和计算机系统和仪表处 实验室、DCRT正在合作一项研究项目，以 在人体内非侵入性地定位癫痫放电来源 脑磁图结合脑磁图 常规脑电(EEG)记录。很多病人 癫痫样障碍表现为低水平的细胞放电 发作间期，表现为发作间期的尖峰或尖波。 他们的脑电和脑磁图(MEG)记录。这个项目 涉及计算机技术的发展，以实现自动化和 增强NINCDS目前使用的程序 神经科医生将确定震源的颅内位置 癫痫患者的癫痫样放电。 在过去的一年里，一种自动检测 脑电和脑磁图信号的癫痫样放电被开发出来 并用从癫痫患者身上获得的样本进行测试。三个- 显示病人头部轮廓的立体显示器 磁力计在上一次安装时的位置 还开发了测量会议，以便神经学家 可以为未来的会议规划传感器位置。一种信号处理 系统中增加了工作站，并将在未来使用 以执行各种信号处理任务。 在未来的一年里，检测计划将进行更多的测试 患者数据，并在工作站上执行，以执行实时 时间检测。检测到的癫痫样的分类方法 基于形态相关的放电类型划分 技术将被开发出来。CT和CT获得的解剖数据 核磁共振扫描将与三维显示器整合在一起 病人头部的轮廓，这样癫痫的来源就可以 与实际的大脑解剖结构相关。

The Medical Neurology Branch, NINCDS, the Biomedical Engineering and Instrumentation Branch, DRS, and the Computer Systems Laboratory, DCRT are collaborating on a research project to noninvasively localize epileptic discharge sources within the human brain by using neuromagnetic recording in conjunction with conventional electroencephalogram (EEG) recording. Many patients with seizure disorders exhibit low-level cellular discharges between seizures, indicated by interictal spikes or sharp waves in their EEG and magnetoencephalogram (MEG) recordings. This project involves the development of computer techniques for automating and enhancing the procedure that is presently used by NINCDS neurologists to determine the intracranial locations of the sources of epileptiform discharges in patients with epilepsy. During the past year, an algorithm that automatically detects the epileptiform discharges from the EEG and MEG signals was developed and tested with siqnals obtained from epileptic patients. Three- dimensional displays of the outline of the patient's head showing where the magnetometer had been positioned during previous measurement sessions were also developed so that the neurologists can plan sensor positions for future sessions. A Signal Processing Workstation was added to the system and will be used in the future to perform various signal processing tasks. In the coming year, the detection program will be tested with more patient data and implemented on the workstation to perform real- time detection. Methods for classifying the detected epileptiform discharges into types based on their morphology using correlation techniques will be developed. Anatomical data obtained from CT and MRI scans will be intergrated with the three-dimensional displays of the outline of the patient's head so that epileptic sources can be represented relative to actual brain anatomy.