Identification of heroin addict pulse signals based on multiwavelet packet transform and support vector machine

Kunbao Cai; Guangtao Zhou

doi:10.1117/12.2521952

17 April 2019 Identification of heroin addict pulse signals based on multiwavelet packet transform and support vector machine

Kunbao Cai, Guangtao Zhou

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Proceedings Volume 11071, Tenth International Conference on Signal Processing Systems; 1107104 (2019) https://doi.org/10.1117/12.2521952
Event: Tenth International Conference on Signal Processing Systems, 2018, Singapore, Singapore

Abstract

The multiwavelet packet transform and support vector machine are applied to identifying human pulse signals of heroin addicts. Firstly, using the multiwavelet packet transform based on the multiwavelet and preprocessing presented by pioneers J. S. Geronimo, D. P. Hardin and P. R. Massopust, the pulse signals of 15 heroin addicts and 15 healthy normal subjects are fully decomposed into three levels. Then, using a technique called the coefficient entropy in the feature extraction for pulse signals, two entropy values of selected coefficients on two frequency bands at the third level are calculated for every pulse signal. Every pair of entropy values is then used to form a two-dimensional feature vector. Lastly, applying the technique of support vector machine, 15 heroin addict vectors and 15 healthy subject feature vectors are successfully separated into two classes. The research results show that the method presented in this paper is really effective for identifying the human pulse signals of heroin addicts.

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Kunbao Cai and Guangtao Zhou "Identification of heroin addict pulse signals based on multiwavelet packet transform and support vector machine", Proc. SPIE 11071, Tenth International Conference on Signal Processing Systems, 1107104 (17 April 2019); https://doi.org/10.1117/12.2521952

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KEYWORDS

Wavelets

Feature extraction

Communication engineering

Analytical research

Time-frequency analysis

Convolution

Discrete wavelet transforms

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