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Use of Frequency Domain to Determine Border of Subthalamic Nucleus: Proof of Concept

Use of Frequency Domain to Determine Border of Subthalamic Nucleus: Proof of Concept. Joshua Hitchins , Hilary W. Thompson, Theodore Weyand, Erich O. Richter LSU Health Sciences Center – New Orleans North American Neuromodulation Society 14 th Annual Meeting, Las Vegas, Nevada

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Use of Frequency Domain to Determine Border of Subthalamic Nucleus: Proof of Concept

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  1. Use of Frequency Domain to Determine Border of Subthalamic Nucleus: Proof of Concept Joshua Hitchins, Hilary W. Thompson, Theodore Weyand, Erich O. Richter LSU Health Sciences Center – New Orleans North American Neuromodulation Society 14th Annual Meeting, Las Vegas, Nevada December 3, 2010

  2. Goal • Explore the use of frequency domain analysis for the purpose of rapid automated discrimination of the border of the subthalamic nucleus (STN)

  3. Introduction • STN is an important target to recognize by microelectrode recordings (MER) during deep brain stimulation (DBS) surgery for Parkinson’s disease (PD) • Fourier analysis (FFT) breaks down an arbitrary signal into a sum of sine and cosine waves and represents the original function by the “weights” associated with the different frequencies

  4. Methods • 1 second segments from each MER electrode, sampled at 22.5 kHz • Multiple segments for repeated samples -- assess variability within a patient at a given depth • Computation of signal variance comparing successive segments for signal stationarity • (Bartlett’s Test for Homogenetiy of Variance)

  5. Methods • Non-stationary signals are rejected • Bartlett’s Kolmogorov-Smirnoff test (1966) • FFT for each 1s segment in MATLAB and Econometric and Time Series Package in SAS • Identify 20 highest energy frequencies at intial (non-STN) depth recordings • Compare values of signal energy variance at successive depths using discriminant analysis with cross validation • Process repeated individually for 5 patients

  6. Results • The discriminant function was successful in distinguishing STN from non-STN in 90 to 96% of samples for the 5 initial patients examined.

  7. Future Directions • Are there consistent frequency ranges that can be examined a priori across patients? • The 1 second segment is arbitrary. Does variation of the duration improve the discrimination accuracy? • Run time application in C++ • Would a discrete wavelet transform (DWT) be more accurate? • Edge discrimination validation

  8. Thank you.

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