Automatic Detection of High-voltage Spindles for Parkinson’s Disease

V. Vigneron, T. Syed, Hsin Chen


Parkinson’s disease is a progressive neurodegenerative disorder which can be characterized by several symptoms such as tremor, slowness of movements, bradykinesia/akinesia and absence of postural reflexes . . . and affects 10 million people worldwide. This paper develops a novel strategy for treating patients with PD: silence High-Voltage-Spindle that resemble the pathophysiological b-waves and contribute to the development of b-waves. Silencing HVSs is expected to delay or even prevent the development of b-waves and thus the progression of PD motor symptoms. High-voltage spindles (HVSs) are observed during waking immobility of patients. In this study, the local field potentials collected from the lesioned and control rats on multiple channels were analyzed with an online detection algorithm to identify the characteristic scillations of HVSs from the second-order statistical properties of the signals and the detection performance is investigated to obtain the optimal choices. These results provide further motivation for the real-time implementation of the automatic HVS detection systems with improved performance for pathophysiological and therapeutic applications to the thalamocortical network dysfunctions.


  1. Clancy, E. and Farry, K. (2000). Adaptive whitening of the electromyogram to improve amplitude estimation. IEEE Trans Biomed Eng., 47(6).
  2. Cover, T. and Thomas, J. (1991). Elements of Information Theory. John Wiley and Sons.
  3. DeFreitas, J., Beck, T., and Stock, M. (2012). Comparison of methods for removing electromagnetic noise from electromyographic signals. Physiological Measurement, 33(2):147.
  4. Denzler, J. and Brown, C. (2002). Information theoretic sensor data selection for active object recognition and state estimation. IEEE trans. on Pat. Anal. and Mach. Intel., 24(2):145-156.
  5. Johnson, B. A., Abramovich, Y. I., and Scharf, L. L. (2011). Detectionestimation of multi-rank gaussian sources using expected likelihood. Digital Signal Processing, 21(5):568 - 575.
  6. Miyano, H., Masuda, T., and Sadoyama, T. (1980). A note on the time constant in low-pass filtering of rectified surface emg. IEEE Trans Biomed Eng., 27(5):274-8.
  7. Perumal, R. and Chen, H. (2014). Performance analysis in a wavelet-based algorithm for automatic detection of high-voltage spindles in parkinson's disease rat models. In 9th Asian-Pacific Conference on Medical and Biological Engineering, Tainan, Taiwan.
  8. Radek, R. J., Curzon, P., and Decker, M. W. (1994). Characterization of high voltage spindles and spatial memory in young, mature and aged rats. Brain Research Bulletin, 33:183188.
  9. Vigneron, V. and Jutten, C. (2004). Independent Component Analysis and Blind Signal Separation, volume LNCS 3195 of Lectures Notes in Computer Science, chapter Fisher Information in Source Separation Problems, pages 168-176. Springer.
  10. Vigneron, V., Syed, T., Montagne, C., Barlovatz-Me imon, G., and Lelandais, S. (2010). Template matching and test detection. Application to cell l ocalization in cells imagery. Pattern Recognition Letters, 31(14):2214- 2224.
  11. Vrins, F., Jutten, C., and Verleysen, M. (2004). Fifth International Conference, ICA 2004, volume LNCS 3195, proceedings Sensor array and electrode selection for non invasive fetal electrocardiogram extraction by Independent Component Ananlysis. Springer-Verlag, Granada, Spain.
  12. Yao, K., Hudson, R., D., C., and Lorenzelli, F. (1998). Blind beamforming on a randomly distributed sensor array system. IEEE Journal on selected areas in communication, 16(8):1555-1566.

Paper Citation

in Harvard Style

Vigneron V., Syed T. and Chen H. (2015). Automatic Detection of High-voltage Spindles for Parkinson’s Disease . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 372-378. DOI: 10.5220/0005328503720378

in Bibtex Style

author={V. Vigneron and T. Syed and Hsin Chen},
title={Automatic Detection of High-voltage Spindles for Parkinson’s Disease},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2015)},

in EndNote Style

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2015)
TI - Automatic Detection of High-voltage Spindles for Parkinson’s Disease
SN - 978-989-758-069-7
AU - Vigneron V.
AU - Syed T.
AU - Chen H.
PY - 2015
SP - 372
EP - 378
DO - 10.5220/0005328503720378