Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is effective treatment of Parkinson disease. Because the STN is small (9 ×7 ×4 mm) and it is not well visible using conventional imaging techniques, multi-microelectrode recordings are used to ensure accurate detection of the STN borders. Commonly used discriminations which microelectrode’s signal relates to the activity of the STN are signal quality and neurologist’s experience dependent. The purpose of this paper is to determine the STN coordinates in a more objective way. We present analysis of the neurological signals acquired during DBS surgeries. The purpose of our method is to discover which one of the scanning microelectrodes reaches the target area guaranteeing a most successful surgery. Signals acquired from microelectrodes are first filtered. Subsequently the spikes are detected and classified. After that, new signal is reconstructed from spikes. This signal’s power is then calculated by means of FFT. Finally cumulative sum of the signal’s power is used to choose a proper electrode.
The ultimate goal of our research is to build a decision support system for the DBS surgery. A successful strategy showing which of the recording microelectrodes should be replaced by the DBS electrode is probably the most difficult and challenging.
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Ciecierski, K., Raś, Z.W., Przybyszewski, A.W. (2011). Selection of the Optimal Microelectrode during DBS Surgery in Parkinson’s Patients. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_59
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DOI: https://doi.org/10.1007/978-3-642-21916-0_59
Publisher Name: Springer, Berlin, Heidelberg
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