0000000000802302
AUTHOR
A. Rosado Munoz
Feature selection for KNN classifier to improve accurate detection of subthalamic nucleus during deep brain stimulation surgery in Parkinson’s patients
The tremor and dystonia associated with Parkinson’s disease can be treated with deep brain stimulation (DBS) implanted into the subthalamic nucleus (STN). The accurate STN detection is a complex neurosurgeon task during a DBS surgery since a proper fixing of stimulating electrodes will impact on the patient’s future life. The brain electrical signals obtained with Micro Electrodes Register (MER) are acquired at different depths of the brain during DBS surgery to detect STN. In our previous work, we found good accuracy performance to improve the localization of STN using K-Nearest Neighbours (KNN) supervised learning algorithm. However, for real-time classification, it is essential to reduce…
From UML State Machine Diagram into FPGA Implementation
Abstract In the paper a method of using the Unified Modeling Language diagrams for specification of digital systems, especially logic controllers, is presented. The proposed method is based mainly on the UML state machine diagrams and uses Hierarchical Concurrent Finite State Machines (HCFSMs) as a temporary model. The paper shows a way to transform the UML diagrams to the form that is acceptable by reconfigurable FPGAs (Field Programmable Gate Arrays). The UML specification is used to generate an effective program in Hardware Description Languages (HDLs), especially Verilog.
STN area detection using K-NN classifiers for MER recordings in Parkinson patients during neurostimulator implant surgery
Deep Brain Stimulation (DBS) applies electric pulses into the subthalamic nucleus (STN) improving tremor and other symptoms associated to Parkinson's disease. Accurate STN detection for proper location and implant of the stimulating electrodes is a complex task and surgeons are not always certain about final location. Signals from the STN acquired during DBS surgery are obtained with microelectrodes, having specific characteristics differing from other brain areas. Using supervised learning, a trained model based on previous microelectrode recordings (MER) can be obtained, being able to successfully classify the STN area for new MER signals. The K Nearest Neighbours (K-NN) algorithm has bee…