| 基于小波变换特征提取的支持向量机心搏分类研究 |
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| 引用本文： | 曹玉珍,李广,范增飞.基于小波变换特征提取的支持向量机心搏分类研究[J].天津大学学报(自然科学与工程技术版),2007,40(7):811-815. |
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| 作者姓名： | 曹玉珍 李广 范增飞 |
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| 作者单位： | 天津大学精密仪器与光电子工程学院 天津300072 |
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| 摘 要： | 在对心电信号进行离散小波变换并提取优化特征组合的基础上,利用标准算法(l-a-r算法)和二叉树算法分别构建支持向量机分类器实现心电图的分类,对不同小波下提取不同维特征向量构建的分类器性能进行比较,同时对取自MIT-BIH数据库的4类心电图(正常心搏、左束支传导阻滞心搏、右束支传导阻滞心搏和起搏心搏)进行分类.结果表明,采用标准算法对db2小波下8维特征向量训练的支持向量机分类器分类性能最优,总体分类正确率达98.770/0.
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| 关 键 词： | 心搏分类 小波变换 特征提取 支持向量机 |
| 文章编号： | 0493-2137（2007）07-0811-05 |
| 修稿时间： | 2006-07-27 |
ECG Beat Classification Using Support Vector Machine with Wavelet Transform Based Feature Extraction |
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| CAO Yu-zhen,LI Guang,FAN Zeng-fei.ECG Beat Classification Using Support Vector Machine with Wavelet Transform Based Feature Extraction[J].Journal of Tianjin University(Science and Technology),2007,40(7):811-815. |
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| Authors: | CAO Yu-zhen LI Guang FAN Zeng-fei |
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| Institution: | School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China |
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| Abstract: | Wavelet transform is applied to electrocardiograph(ECG) beat and the optimized feature combinations are obtained by feature searching algorithm.Support vector machine(SVM) classifiers are trained by using l-against-rest(l-a-r) algorithm and binary tree algorithm for ECG beat classification.The capabilities of classifiers using different feature vectors with different wavelets are compared.Four types of ECG beats(normal beat,left bundle branch block beat,right bundle branch block beat and paced beat) obtained from MIT-BIH database are classified by the algorithms.The results show that the classifier trained by 8 dimensional feature vectors based on db2 wavelet using l-a-r algorithm has the best performance with an accuracy of 98.770/0. |
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| Keywords: | electrocardiograph(ECG) beat classification wavelet transform feature extraction support vector machine |
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