Time-frequency Analysis of Brain Electrical Signals for behvior Recognition in Patients with Parkinson's Disease

被引：0

作者：

Jiang, Huaiguang ^{[1
]}

Zhang, Jun Jason ^{[1
]}

Hebb, Adam ^{[2
]}

Mahoor, Mohammad H. ^{[1
]}

机构：

[1] Univ Denver, Dept Elect & Comp Engn, Denver, CO 80210 USA

[2] Colorado Neurol Inst, Englewood, CO 80113 USA

来源：

2013 ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS | 2013年

关键词：

Matching pursuit decomposition; hidden Markov model; local field potential; deep brain stimulation; Parkinson's disease; HUMAN SUBTHALAMIC NUCLEUS; BETA;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1843 / 1847

页数：5

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