Vargas Cardona Hernán Darío, Padilla Jose Bestier, Arango Ramiro, Carmona Hans, Álvarez Mauricio A, Guijarro Estellés Enrique, Orozco Álvaro Ángel
Department of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira, Colombia.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2219-22. doi: 10.1109/EMBC.2012.6346403.
The success of stereotactic surgery for Deep Brain Stimulation depends critically on the exact positioning of a microelectrode recording in a target area of the brain. This paper presents the software system NEUROZONE composed of two main applications: first, it allows online recognition of brain structures by the analysis of signals from microelectrode recordings (MER), and second, it processes and analyses off-line databases allowing the inclusion of new trained classifiers for automatic identification. The software serves as a support to the analysis done by a medical specialist during surgery, and seeks to reduce the adverse side effects that may occur because of inadequate identification of the target areas. The software also allows the specialists to label recordings obtained during surgery, in order to generate a new off-line database or increase the amount of records in an already existing off-line database. NEUROZONE has been tested for Deep Brain Stimulation performed at the Institute for Epilepsy and Parkinson of the Eje Cafetero (Colombia), achieving positive identifications of the Subthalamic Nucleus (STN) over to 85% using a naive Bayes classifier.
立体定向手术用于脑深部电刺激的成功与否关键取决于微电极记录在脑目标区域的精确定位。本文介绍了由两个主要应用程序组成的软件系统NEUROZONE：其一，它通过分析微电极记录（MER）的信号实现脑结构的在线识别；其二，它处理和分析离线数据库，并允许纳入新的经过训练的分类器以进行自动识别。该软件为医学专家在手术期间进行的分析提供支持，并试图减少因目标区域识别不足可能出现的不良副作用。该软件还允许专家标记手术期间获得的记录，以便生成新的离线数据库或增加现有离线数据库中的记录数量。NEUROZONE已在哥伦比亚咖啡区癫痫与帕金森研究所进行的脑深部电刺激测试中得到验证，使用朴素贝叶斯分类器对丘脑底核（STN）的正确识别率超过85%。
