A model for automatic identification of human pulse signals |
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| Authors: | Hui-yan Wang and Pei-yong Zhang |
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| Institution: | [1]College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China [2]Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China |
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| Abstract: | This paper presents a quantitative method for automatic identification of human pulse signals. The idea is to start with the extraction of characteristic parameters and then to construct the recognition model based on Bayesian networks. To identify depth, frequency and rhythm, several parameters are proposed. To distinguish the strength and shape, which cannot be represented by one or several parameters and are hard to recognize, the main time-domain feature parameters are computed based on the feature points of the pulse signal. Then the extracted parameters are taken as the input and five models for automatic pulse signal identification are constructed based on Bayesian networks. Experimental results demonstrate that the method is feasible and effective in recognizing depth, frequency, rhythm, strength and shape of pulse signals, which can be expected to facilitate the modernization of pulse diagnosis. |
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| Keywords: | Pulse signal identification Feature extraction Bayesian network Quantitative diagnosis Wavelet transform |
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