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Detection of SNr Recordings Basing upon Spike Shape Classes and Signal’s Background

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Brain Informatics and Health (BIH 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9919))

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Abstract

During deep brain stimulation (DBS) surgery for Parkinson disease, the target is the subthalamic nucleus (STN). STN is small, (\(9\times 7\times 4\) mm) and typically localized by a series of parallel microelectrodes. As those electrodes are in steps advanced towards and through the STN, they record the neurobiological activity of the surrounding tissues. The electrodes are advanced until they pass through the STN and/or they reach the Substantia Nigra pars reticulata (SNr). There is no necessity of going further as the SNr lies ventral to the STN. There are good classification methods for detection weather given recording comes from the STN or not, they still do sometimes falsely identify SNr recordings as STN ones. This paper focuses on method devised for SNr detection, specifically on detection if given recording bears characteristics typical for SNr. Presented method relies on spike sorting and assessing characteristics of the obtained spike shape classes together with the enhanced analysis of the signal’s background computed by the STN classification methods described in [8–12].

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Authors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, 00-655, Warsaw, Poland

    Konrad A. Ciecierski

  2. Department of Neurosurgery, M. Sklodowska-Curie Memorial Oncology Center, Warsaw, Poland

    Tomasz Mandat

  3. Department of Neurosurgery, Institute of Psychiatry and Neurology in Warsaw, Warsaw, Poland

    Tomasz Mandat

Authors
  1. Konrad A. Ciecierski
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  2. Tomasz Mandat
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Corresponding author

Correspondence to Konrad A. Ciecierski .

Editor information

Editors and Affiliations

  1. George Mason University, Fairfax, Virginia, USA

    Giorgio A. Ascoli

  2. Department of Modeling, Analysis and Theory, Allen Institute for Brain Science, Seattle, Washington, USA

    Michael Hawrylycz

  3. University of Nebraska Omaha, Omaha, Nebraska, USA

    Hesham Ali

  4. Information Science and Technology, University of Nebraska Omaha, Omaha, Nebraska, USA

    Deepak Khazanchi

  5. College of Information Sciences and Technology, University of Nebraska Omaha, Omaha, Nebraska, USA

    Yong Shi

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Ciecierski, K.A., Mandat, T. (2016). Detection of SNr Recordings Basing upon Spike Shape Classes and Signal’s Background. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_33

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  • DOI: https://doi.org/10.1007/978-3-319-47103-7_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47102-0

  • Online ISBN: 978-3-319-47103-7

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