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

This study explores using frequency domain analysis to efficiently detect the border of the subthalamic nucleus during deep brain stimulation surgery for Parkinson's disease. The method involves Fourier analysis, signal variance computation, and discriminant analysis to distinguish STN from non-STN with high success rates in initial testing of patients. Future directions include exploring consistent frequency ranges, duration variations, implementing C++ application, assessing discrete wavelet transform accuracy, and edge discrimination validation.

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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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