Abstract
Parkinsons disease is a complex neurodegenerative disorder for which patients present many symptoms, tremor being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the patients state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the patient, stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of Parkinsons disease and particularly the tremor. The goal of the designed system is to detect the moments the patient is suffering from a tremor episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten Parkinsonian patients, who were diagnosed with tremor-dominant Parkinsons disease and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying tremor episodes. Results are favourable, reaching accuracies of higher 98.7 % in 70 % of the patients.
References
Bakstein, E., Burgess, J., Warwick, K., Ruiz, V., Aziz, T., and Stein, J., Parkinsonian tremor identification with multiple local field potential feature classification. J. Neurosci. Methods 209(2):320–330, 2012.
Buckley, J. J., and Eslami, E., An introduction to fuzzy logic and fuzzy sets. Vol. 13: Springer Science & Business Media, 2002.
Camara, C., Isasi, P., Warwick, K., Ruiz, V., Aziz, T., Stein, J., and Baktein, E., Resting tremor classification and detection in parkinson’s disease patients. Biomed. Signal Process. Control 16(0):88–97, 2015.
Gasson, M. N., Wang, S. Y., Aziz, T. Z., Stein, J. F., and Warwick, K., Towards a demand driven deep-brain stimulator for the treatment of movement disorders (2005)
Jantzen, J., Design Of Fuzzy Controllers 98:1–28, 1998.
Lachaux, J. P., Rodriguez, E., Martinerie, J., Varela, F. J., et al., Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8(4):194–208, 1999.
Lees, A. J., Hardy, J., and Revesz, T., Parkinson’s disease. The Lancet 373(9680):2055–2066, 2009.
Medtronic.com. http://www.medtronic.com/patients/parkinsons-disease/living-with/replacement/
Mendel, J. M., Tutorial on higher-order statistics spectra in signal processing and system theory: theoretical results and some applications. Proc. IEEE 79(3):278–305, 1991.
Myers, L. J., Lowery, M., O’Malley, M., Vaughan, C. L., Heneghan, C., St. Clair Gibson, A., Harley, Y. X. R., and Sreenivasan, R., Rectification and non-linear pre-processing of emg signals for cortico-muscular analysis. J. Neurosci. Methods 124(2):157–165, 2003.
Nof, S. Y., Springer Handbook of Automation: Springer Publishing Company, Incorporated, 2009.
Nutt, J. G., Levodopa-induced dyskinesia review, observations, and speculations. Neurology 40(2):340–340, 1990.
Nutt, J. G., Carter, J. H., and Woodward, W. R., Long-duration response to levodopa. Neurology 45(8):1613–1616, 1995.
Pan, S., Iplikci, S., Warwick, K., and Aziz, T., Parkinsons disease tremor classification–a comparison between support vector machines and neural networks. Expert Syst. Appl. 39(12):10764–10771, 2012.
Pereda, E., Quiroga, R. Q., and Bhattacharya, J., Nonlinear multivariate analysis of neurophysiological signals. Prog. Neurobiol. 77(1):1–37, 2005.
Perlmutter, J. S., and Mink, J. W., Deep brain stimulation. Annu. Rev. Neurosci. 29:229–257, 2006.
Quian Quiroga, R., Kraskov, A., Kreuz, T., and Grassberger, P., Performance of different synchronization measures in real data: a case study on electroencephalographic signals. Phys. Rev. E 65(4):041903, 2002.
Rajput, A. H., and Birdi, S., Epidemiology of parkinson’s disease. Parkinsonism Relat. Disord. 3(4):175–186, 1997.
Rosin, B., Slovik, M., Mitelman, R., Rivlin-Etzion, M., Haber, S., Israel, Z., Vaadia, E., and Bergman, H., Closed-loop deep brain stimulation is superior in ameliorating parkinsonism. Neuron 72(2):370–384, 2011.
Santaniello, S., Fiengo, G., Glielmo, L., and Grill, W. M., Closed-loop control of deep brain stimulation: a simulation study. IEEE Trans. Neural Syst. Rehabil. Eng. 19(1):15–24, 2011.
Wong, D., Clifton, D. A., and Tarassenko, L., An introduction to the bispectrum for eeg analysis. In: Postgraduate Conference in Biomedical Engineering & Medical Physics, p. 61 (2009)
Wu, D., Warwick, K., Ma, Z., Gasson, M. N., Burgess, J. G., Pan, S., and Aziz, T. Z., Prediction of Parkinson’s disease tremor onset using a radial basis function neural network based on particle swarm optimization. Int. J. Neural Syst. 20(2):109–116, 2010.
Yao, B., Salenius, S., Yue, G. H., Brown, R. W., and Liu, J. Z., Effects of surface emg rectification on power and coherence analyses: an eeg and meg study. J. Neurosci. Methods 159(2):215–223, 2007.
Acknowledgments
This work was supported by The Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)( http://www.ciber-bbn.es/)
Conflict of interests
The author declares that they have no conflict of interest.
Author information
Authors and Affiliations
Corresponding author
Additional information
This article is part of the Topical Collection on Patient Facing Systems
Rights and permissions
About this article
Cite this article
Camara, C., Warwick, K., Bruña, R. et al. A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson’s Disease. J Med Syst 39, 155 (2015). https://doi.org/10.1007/s10916-015-0328-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-015-0328-x