Adaptive learning of behavioral tasks for patients with Parkinson's disease using signals from deep brain stimulation

N. Zaker, A. Dutta, A. Maurer, J. J. Zhang, S. Hanrahan, A. O. Hebb, N. Kovvali, Antonia Papandreou-Suppappola

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

We propose adaptive learning methods for identifying different behavioral tasks of patients with Parkinson's disease (PD). The methods use local field potential (LFP) signals that were collected during Deep Brain Stimulation (DBS) implantation surgeries. Using time-frequency signal processing methods, features are first extracted and then clustered in the feature space using two different methods. The first method requires training and uses a hybrid model that combines support vector machines and hidden Markov models. The second method does not require any a priori information and uses Dirichlet process Gaussian mixture models. Using the DBS acquired signals, we demonstrate the performance of both methods in clustering different behavioral tasks of PD patients and discuss the advantages of each method under different conditions.

Original languageEnglish (US)
Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages208-212
Number of pages5
ISBN (Electronic)9781479982974
DOIs
StatePublished - Apr 24 2015
Event48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 2 2014Nov 5 2014

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2015-April
ISSN (Print)1058-6393

Other

Other48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
CountryUnited States
CityPacific Grove
Period11/2/1411/5/14

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Zaker, N., Dutta, A., Maurer, A., Zhang, J. J., Hanrahan, S., Hebb, A. O., Kovvali, N., & Papandreou-Suppappola, A. (2015). Adaptive learning of behavioral tasks for patients with Parkinson's disease using signals from deep brain stimulation. In M. B. Matthews (Ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers (pp. 208-212). [7094429] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2014.7094429