A new method of tremor diagnosis based on singular value decomposition of EMD.
- Author:
Lingmei AI
1
;
Jue WANG
Author Information
1. Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong University, Xi'an 710049, China. almsac@yahoo.com.cn
- Publication Type:Journal Article
- MeSH:
Algorithms;
Arm;
physiopathology;
Diagnosis, Computer-Assisted;
Diagnosis, Differential;
Electromyography;
methods;
Humans;
Muscle, Skeletal;
physiology;
Parkinson Disease;
diagnosis;
Signal Processing, Computer-Assisted;
Software;
Support Vector Machine;
Tremor;
diagnosis
- From:
Journal of Biomedical Engineering
2009;26(6):1335-1339
- CountryChina
- Language:Chinese
-
Abstract:
Aiming at three kinds of tremor, including essential tremor (ET), Parkinsonian disease (PD) tremor and physiological tremor (PT), which are subjected to frequent clinical misdiagnosis, a new method based on singular value decomposition (SVD) of empirical mode decomposition (EMD) and support vector machine (SVM) for the recognition of tremor is proposed in this paper. First, the hand acceleration signals of three different types of 40 tremor voluntary subjects were collected on the basis of informed consent, and the EMD method was used to decompose the signals into a number of stationary intrinsic mode functions (IMFs). Then the preceding four IMFs which could describe signals were selected, and the initial feature vector matrixes were formed. After the application of SVD technique to the initial feature vector matrixes, the singular values were obtained and used as the feature vectors of tremor types to be put in the support vector machine classifier as well as in the identification of tremor types. The results of experiment have shown that the proposed diagnosis method based on SVD of EMD and SVM can extract tremor features effectively and identify tremor types accurately. It also provides a new assistant approach for clinical diagnosis of tremor.
