| 基于最大散度的特征搜索算法用于心搏分类的研究 |
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| 引用本文： | 曹玉珍,范增飞.基于最大散度的特征搜索算法用于心搏分类的研究[J].生物医学工程学杂志,2008,25(1):53-56. |
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| 作者姓名： | 曹玉珍 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： | 在对心电图进行离散小波变换获得特征空间的基础上,提出了基于最大散度的特征搜索算法.对特征空间进行搜索得到不同维数下的优化特征组合,通过研究这些优化特征组合的散度值随维数的变化趋势,最终确定特征向量的特征构成,并以此特征向量训练BP神经网络.取自MIT-BIH数据库的四类心电图(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)的分类正确率达到93.9%,检出率较高.
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| 关 键 词： | 心电图分类 神经网络 小波变换 特征提取 散度 散度 特征向量 搜索算法 分类 研究 Divergence Maximal Based Algorithm Searching Feature 检出率 正确率 起搏 右束支传导阻滞 左束支传导阻滞 数据库 神经网络 训练 构成 |
| 文章编号： | 1001-5515（2008）01-0053-04 |
| 收稿时间： | 2005-11-09 |
| 修稿时间： | 2006-02-23 |
ECG Pattern Classification by Feature Searching Algorithm Based on Maximal Divergence |
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| Cao Yuzhen,Fan Zengfei.ECG Pattern Classification by Feature Searching Algorithm Based on Maximal Divergence[J].Journal of Biomedical Engineering,2008,25(1):53-56. |
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| Authors: | Cao Yuzhen Fan Zengfei |
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| Institution: | College of Precision Instrument & Opto-electmrnics Engineering, Tianjin University, Tianjin 300072, China. yzcao@tju.edu.cn |
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| Abstract: | This paper presents a method of using feature searching algorithm based on maximal divergence value to get the optimized feature combinations at different dimensions from feature space.Feature space is obtained through wavelet transform on ECG beat.Then the feature vector is determined by analyzing the changes of divergence value of those optimized feature combinations along with the dimensions.BP artificial neural network is trained by the feature vector and 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 with a success of 93.9%. |
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| Keywords: | ECG classification Neural network Wavelet transform Feature extraction Divergence |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
