Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA.
Neurosurgery. 2010 Mar;66(3 Suppl Operative):108-17; discussion 117. doi: 10.1227/01.NEU.0000365369.48392.E8.
Placement of deep brain stimulators (DBSs) currently involves the use of both image-based stereotaxy and intraoperative microelectrode recording (MER). Interpretations of MER data and integration with anatomical data are currently manual processes. Hidden Markov models (HMMs) are commonly used in signal processing, speech recognition, and a wide array of biologic applications.
A 6-state HMM was designed and trained for evaluation in simulated surgery for subthalamic nucleus (STN) DBS.
The accuracy of identifying the correct brain location was 98.5%. Sensitivity of detecting passes intersecting the STN was 100%, and specificity was 84.9%. Anatomical location of the MER passes was calculated with a mean error of 0.06 mm (95% confidence interval, -0.54 to 0.42 mm) in the medial-lateral axis.
Automated DBS intraoperative navigation using HMMs may be feasible based on promising results of this prototype system.
目前深部脑刺激器（DBS）的放置涉及使用基于图像的立体定向和术中微电极记录（MER）。MER 数据的解释和与解剖数据的整合目前是手动过程。隐马尔可夫模型（HMM）常用于信号处理、语音识别以及广泛的生物应用中。
设计并训练了一个 6 状态 HMM，用于评估模拟的丘脑底核（STN）DBS 手术。
正确识别大脑位置的准确率为 98.5%。检测穿过 STN 的通道的灵敏度为 100%，特异性为 84.9%。MER 通道的解剖位置计算的平均误差为 0.06 毫米（95%置信区间，-0.54 至 0.42 毫米）在中-外侧轴上。
基于该原型系统的有希望的结果，使用 HMM 进行自动 DBS 术中导航可能是可行的。
