Skip to main content

Advertisement

SpringerLink
Log in

An Automatic Algorithm for Stationary Segmentation of Extracellular Microelectrode Recordings

  • Technical Note
  • Published:
Medical and Biological Engineering and Computing Aims and scope Submit manuscript

Abstract

Extracellular microelectrode recordings (MER) often contain artifact from a variety of sources that confound traditional signal-processing techniques that require stationary signal segments. We designed an algorithm to locate the longest stationary segment of MER signals. In this paper we provide a description of the segmentation algorithm and its performance assessment. Simulation results demonstrate that the automatic segmentation algorithm we proposed is capable of accurately identifying the boundaries of the longest stationary segments in MER signals. In our simulation study the segmentation algorithm correctly identified the boundaries of the longest MER stationary segments in 99.5% of the cases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Burchiel KJ, Anderson VC, Favre J, Hammerstad JP (1999) Comparison of pallidal and subthalamic nucleus deep brain stimulation for advanced Parkinson’s disease: results of a randomized, blinded pilot study. Neurosurgery 45(6):1375–1384

    Article  Google Scholar 

  2. Favre J, Taha JM, Baumann T, Burchiel KJ (1999) Computer analysis of the tonic, phasic, and kinesthetic activity of pallidal discharges in Parkinson patients. Surg Neurol 51:665–673

    Article  Google Scholar 

  3. Lang AE, Lozano AM (1998) Parkinson’s diease. First of two parts. N Engl J Med 339:1044–1053

    Article  Google Scholar 

  4. Lang AE, Lozano AM (1998) Parkinson’s diease. Second of two parts. N Engl J Med 339:1130–1143

    Article  Google Scholar 

  5. Vitek JL, Bakay RA, Hashimoto T, Kaneoke Y, Mewes K, Zhang JY, Rye D, Starr P, Baron M, Turner R, DeLong MR (1998) Microelectrode-guided pallidotomy: technical approach and its application in medically intractable Parkinson’s disease. J Neurosurg 88:1027–1043

    Article  Google Scholar 

  6. Zonenshayn M, Rezai AR, Mogilner AY, Beric A, Sterio D, Kelly PJ (2000) Comparison of anatomic and neurophysiological methods for subthalamic nucleus targeting. Neurosurgery 47(2):282–294

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics Engineering Technology, Oregon Institute of Technology, Portland, OR, 97006, USA

    Mateo Aboy

  2. University of Washington, Seattle, WA, USA

    J. Haakon Falkenberg

Authors
  1. Mateo Aboy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Haakon Falkenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mateo Aboy.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Aboy, M., Falkenberg, J.H. An Automatic Algorithm for Stationary Segmentation of Extracellular Microelectrode Recordings. Med Bio Eng Comput 44, 511–515 (2006). https://doi.org/10.1007/s11517-006-0052-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-006-0052-2

Keywords

Search

Navigation