Title :
Time-frequency analysis of brain electrical Signals for behvior recognition in patients with Parkinson´s disease
Author :
Huaiguang Jiang ; Zhang, J.J. ; Hebb, Adam ; Mahoor, M.H.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Denver, Denver, CO, USA
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.
Keywords :
behavioural sciences; bioelectric potentials; brain; diseases; feature extraction; hidden Markov models; iterative methods; learning (artificial intelligence); medical signal processing; neurophysiology; time-frequency analysis; DBS system; LFP signals; MPD algorithm; PD patient behaviors; Parkinson disease; STN area; amplitude-time-frequency-variance feature extraction; behavior recognition; brain electrical signals; continuous HMM; deep brain stimulation system; discrete HMM; feature vectors; hidden Markov models; human behviors; local field potential signals; machine learning techniques; matching pursuit decomposition; subthalamic area; time-frequency analysis; Atomic clocks; Feature extraction; Hidden Markov models; Matching pursuit algorithms; Satellite broadcasting; Time-frequency analysis; Vectors; Matching pursuit decomposition; Parkinson´s disease; deep brain stimulation; hidden Markov model; local field potential;
Conference_Titel :
Signals, Systems and Computers, 2013 Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
978-1-4799-2388-5
DOI :
10.1109/ACSSC.2013.6810621
