Characterization of subcortical structures during deep brain stimulation utilizing support vector machines.

  1. Guillén, P. 1
  2. Martínez-de-Pisón, F. 2
  3. Cordero Sánchez, Reinaldo . 1
  4. Argáez, M. 1
  5. Velázquez, L. 1
  1. 1 University of Texas at El Paso
    info

    University of Texas at El Paso

    El Paso, Estados Unidos

    ROR https://ror.org/04d5vba33

  2. 2 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Buch:
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference

ISBN:

Datum der Publikation: 2011

Ausgabe: 2011

Seiten: 7949-7952

Art: Buch-Kapitel

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DOI: 10.1109/IEMBS.2011.6091960 PMID: 22256184 SCOPUS: 2-s2.0-84861702712 WoS: WOS:000298810006018 GOOGLE SCHOLAR

Zusammenfassung

In this paper we discuss an efficient methodology for the characterization of Microelectrode Recordings (MER) obtained during deep brain stimulation surgery for Parkinson's disease using Support Vector Machines and present the results of a preliminary study. The methodology is based in two algorithms: (1) an algorithm extracts multiple computational features from the microelectrode neurophysiology, and (2) integrates them in the support vector machines algorithm for classification. It has been applied to the problem of the recognition of subcortical structures: thalamus nucleus, zona incerta, subthalamic nucleus and substantia nigra. The SVM (support vector machines) algorithm performed quite well achieving 99.4% correct classification. In conclusion, the use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity in the localization of the subcortical structures and mainly the subthalamic nucleus (STN) for neurostimulation.

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This work was supported in part by the U.S. Army Research Laboratory, through the Army High Performance Computing Research Center, Cooperative Agreement W911NF-07-0027, Universidad de Los Andes, and the Program in Computational Science at the University of Texas at El Paso. The author thanks the Institute of Parkinson and Epilepsy of the Eje Cafetero of Pereira in Colombia for supplying the data recordings.

Geldgeber

  • U.S. Army Research Laboratory, through the Army High Performance Computing Research Center
    • W911NF-07-0027
  • Universidad de Los Andes
  • Program in Computational Science at the University of Texas at El Paso