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Multi-features Fusion Diagnosis Of Tremor Based On Artificial Neural Network And D-s Evidence Theory

机译：基于人工神经网络和D-s证据理论的震颤多特征融合诊断

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摘要

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.
机译：针对频繁发生临床误诊的原发性震颤（ET），帕金森病（PD）震颤和生理性震颤（PT）三种常见的震颤，一种基于多特征的震颤融合诊断新方法提出了提取，反向传播神经网络（BPNN）和Dempster-Shafer（DS）证据理论来克服临床误诊。首先，分别使用双频谱，经验模态分解（EMD）和离散小波变换（DWT）分析方法提取具有ET，PD和PT的受试者的手部加速度信号的特征。其次，生成的特征随后分别被三个独立的BPNN识别，其输出被进一步处理，并作为震颤的基本概率分配。最后，将基本概率分配与D-S证据理论融合，并进行决策分析。实验分析结果表明，D-S证据理论融合结果的准确性明显高于BPNN的独立诊断。本文提出的方法能够充分利用互补的多特征信息来准确识别震颤类型，从而为临床诊断震颤类型提供了实用的指导意义。

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