Multi-features fusion diagnosis of tremor based on artificial neural network and D–S evidence theory

• Published in: Signal processing Vol. 88; no. 12; pp. 2927 - 2935
• Main Authors: Ai, Lingmei, Wang, Jue, Wang, Xuelian
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2008; Elsevier Science
• Subjects: Applied sciences; Artificial intelligence; Back-propagation neural network; Biological and medical sciences; Bispectrum; Computer science; control theory; systems; Computerized, statistical medical data processing and models in biomedicine; Connectionism. Neural networks; Discrete wavelet transform; D–S evidence theory; Empirical mode decomposition; Exact sciences and technology; Information, signal and communications theory; Medical management aid. Diagnosis aid; Medical sciences; Miscellaneous; Signal processing; Telecommunications and information theory; Tremor diagnosis; Back-propagation neural network; Empirical mode decomposition; Discrete wavelet transform; Tremor diagnosis; Bispectrum; D–S evidence theory; Disease; Decision support system; D-S evidence theory; Decomposition method; Empirical method; Neural network; Discrete wavelet transforms; Backpropagation algorithm; Accuracy; Evidence theory; Dempster Shafer theory; Signal processing; Feature extraction; Diagnostic aid
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2008.06.018

With respect to three kinds of familiar tremor, including essential tremor (ET), Parkinsonian disease (PD) tremor, and physiological tremor (PT), which are subjected to frequent clinical misdiagnosis, a new fusion diagnosis method for tremor based on multi-features extraction, back-propagation neural network (BPNN), and Dempster–Shafer (D–S) evidence theory is proposed to overcome the clinical misdiagnosis. First the features of hand acceleration signals of subjects with ET, PD, and PT were extracted using bispectrum, empirical mode decomposition (EMD) and discrete wavelet transform (DWT) analysis methods, respectively. Second the resulting features were subsequently recognized by three independent BPNNs, respectively, the outputs of which were further processed and acted as basic probability assignments for tremor. Finally, the basic probability assignments were fused by the D–S evidence theory and decision-making analysis was performed. The experimental analysis results indicate that the accuracy of fusion results of the D–S evidence theory is markedly higher than the independent diagnosis of BPNN. The method proposed in this paper is able to adequately utilize the complementary multi-features information for accurately recognizing tremor types, thus providing practical guiding significance for diagnosing tremor types in clinic.