Abstract
In neurosurgical treatment of the Parkinson Disease (PD) the target is a small (9 x 7 x 4 mm) deep within brain placed structure called Subthalamic Nucleus (STN). The goal of the Deep Brain Stimulation (DBS) surgery is the permanent precise placement of the stimulating electrode within target nucleus. As this structure poorly discriminates in CT or MRI it is usually stereotactically located using microelectrode recording. Several microelectrodes are parallelly inserted into the brain and in measured steps they are advanced towards expected location of the nucleus. At each step, from 20 mm above the target, the neuronal activity is recorded. Because STN has a distinct physiology, the signals recorded within it also present specific features. By extracting certain features from recordings provided by the microelectrodes, it is possible to construct a classifier that provides useful discrimination. This discrimination divides the recordings into two classes, i.e. those registered within the STN and those registered outside of it. Using the decision tree based classifiers, the best results have been obtained using the Random Forest method. In this paper we compared the results obtained from the Random Forest to those provided by the classification based upon rules extracted by the rough set approach.
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Ciecierski, K., Raś, Z.W., Przybyszewski, A.W. (2014). Intraoperative Decision Making with Rough Set Rules for STN DBS in Parkinson Disease. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_30
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DOI: https://doi.org/10.1007/978-3-319-09891-3_30
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