Paper Info
Title
Time-Frequency Analysis Of Brain Electrical Signals For Behvior Recognition In Patients With Parkinson'S Disease
Abstract
A behvior recognition approach is proposed based on time-frequency analysis and machine learning techniques to identify Parkinson's disease (PD) patients' behviors using local field potential (LFP) signals obtained from a deep brain stimulation (DBS) system. Specifically, the amplitude-time-frequency-variance features are extracted by the matching pursuit decomposition (MPD) algorithm from LFP signals sampled by a DBS lead from the subthalamic (STN) area. Using the extracted feature vectors, different hidden Markov models (HMMs) including discrete and continuous HMMs are trained and then used to recognize different human behviors. The experiment results demonstrate the feasibility, effectiveness and accuracy of our proposed method.
Year
DOI
Venue
2013
10.1109/ACSSC.2013.6810621
2013 ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS
Keywords
Field
DocType
Matching pursuit decomposition, hidden Markov model, local field potential, deep brain stimulation, Parkinson's disease
Deep brain stimulation,Feature vector,Signal,Parkinson's disease,Pattern recognition,Computer science,Speech recognition,Feature extraction,Local field potential,Time–frequency analysis,Artificial intelligence,Hidden Markov model
Conference
ISSN
Citations 
PageRank 
1058-6393
3
0.54
References 
Authors
2
4
Name
Order
Citations
PageRank
Huaiguang Jiang1245.11
Jun Jason Zhang212218.78
Adam Hebb382.09
Mohammad H. Mahoor486155.59