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A Computationally-Efficient, Online-Learning Algorithm for Detecting High-Voltage Spindles in the Parkinsonian Rats.

Ramesh Perumal1, Vincent Vigneron2,3, Chi-Fen Chuang4

  • 1Department of Electrical Engineering, National Tsing Hua University, No.101, Sec.2 Kuang-Fu Road, Hsinchu, 30013, Taiwan, R.O.C.

Annals of Biomedical Engineering
|November 17, 2020
PubMed
Summary

This study presents a novel algorithm for detecting high-voltage spindles (HVSs) in parkinsonian rats. The method enables adaptive deep brain stimulation (DBS) to mitigate motor deficits with high accuracy and low latency.

Keywords:
Adaptive Kalman filterAutoregressive modelingClosed-loop deep brain stimulationHilbert-Huang transformParkinson’s diseaseSmart neuromodulator

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • High-voltage spindles (HVSs) are linked to motor deficits in parkinsonian rats.
  • Current deep brain stimulation (DBS) for Parkinson's disease can cause side effects due to continuous stimulation.

Purpose of the Study:

  • To develop a low-latency algorithm for detecting HVSs to enable adaptive DBS.
  • To create a hardware-friendly algorithm for an adaptive neuromodulator.

Main Methods:

  • Proposed an algorithm based on autoregressive modeling with online parameter learning via an adaptive Kalman filter.
  • Tested the algorithm on local field potentials (LFPs) from parkinsonian rats containing 1131 HVS episodes.

Main Results:

  • Achieved 100% sensitivity in detecting all HVS episodes.
  • Demonstrated 96% precision and a low latency of 61 ms, outperforming continuous wavelet transform.
  • The algorithm requires less computation time compared to existing methods.

Conclusions:

  • The developed algorithm effectively detects HVSs with high sensitivity, precision, and low latency.
  • This algorithm is suitable for creating smart neuromodulators for closed-loop DBS to mitigate HVSs and associated motor deficits.