Arteriosclerosis Diagnosis Based on Support Vector Machine

被引：4

作者：

Zhang, Yue ^{[1
]}

Pan, Junjun ^{[1
]}

机构：

[1] Tsinghua Univ, Lab Embedded Syst & Technol, Grad Sch Shenzhen, Shenzhen, Peoples R China

来源：

2018 5TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE 2018) | 2018年

关键词：

diagnosis of arteriosclerosis; photoplethysmogram; support vector machine; genetic algorithms; feature selection;

D O I：

10.1109/ICISCE.2018.00038

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we describe a new method for diagnosing arteriosclerotic disease using photoplethysmogram (PPG) signal. In this method, three types of characteristic parameters are used, including waveform parameters in the time domain, harmonic amplitudes in the frequency domain, and wavelet energy characteristics. Among these characteristic parameters, we have selected some features that are more favorable for the diagnosis of arteriosclerosis based on genetic algorithms. The support vector machine (SVM) model is used to fuse these parameters to establish a non-invasive arterial sclerosis recognition model. The results show that the accuracy of our algorithm on the test set is not less than 98%, and the accuracy rate on the training set reaches 100.0%. The new algorithm is accurate and convenient, and has a strong practical application potential.

引用

页码：138 / 142

页数：5

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