TY - GEN
T1 - Adaptive learning of behavioral tasks for patients with Parkinson's disease using signals from deep brain stimulation
AU - Zaker, N.
AU - Dutta, A.
AU - Maurer, A.
AU - Zhang, J. J.
AU - Hanrahan, S.
AU - Hebb, A. O.
AU - Kovvali, N.
AU - Papandreou-Suppappola, Antonia
PY - 2015/4/24
Y1 - 2015/4/24
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84940572803&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84940572803&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2014.7094429
DO - 10.1109/ACSSC.2014.7094429
M3 - Conference contribution
AN - SCOPUS:84940572803
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 208
EP - 212
BT - Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Y2 - 2 November 2014 through 5 November 2014
ER -