[1]葛丁飞.基于Frank导联的心肌梗死不同阶段自动分类[J].深圳大学学报理工版,2009,26(2):137-142.
 GE Ding-fei.Automatic discrimination for different stages in myocardial infarction based on Frank leads[J].Journal of Shenzhen University Science and Engineering,2009,26(2):137-142.
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基于Frank导联的心肌梗死不同阶段自动分类()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第26卷
期数:
2009年2期
页码:
137-142
栏目:
电子与信息工程
出版日期:
2009-04-30

文章信息/Info

Title:
Automatic discrimination for different stages in myocardial infarction based on Frank leads
文章编号:
1000-2618(2009)02-0137-06
作者:
葛丁飞
浙江科技学院信息与电子工程学院,杭州 310012
Author(s):
GE Ding-fei
School of Information and Electronic Engineering,Zhejiang University of Science and Technology,Hangzhou 310012,P.R.China
关键词:
心肌梗死心电图Frank导联特征提取
Keywords:
myocardial infarctionelectrocardiogramFrank leadsfeature extraction
分类号:
R 318.04;R 540.41
文献标志码:
A
摘要:
基于Frank 正交心电导联早期心肌梗死的特征提取和分类,提出利用多通道回归(multivariate autoregressive,MAR)模型对心电信号(electrocardiogram,ECG)进行建模,以MAR系数为心电特征,对PTB诊断数据库中的正常状态病人、早期心肌梗死和急性期心肌梗死进行分类测试.结果表明,利用该方法从Frank心电导联中提取特征对早期心肌梗死和急性期心肌梗死进行分类诊断是可行的,分类精度能获得有效提高.
Abstract:
Most of existing myocardial infarction (MI) techniques focus on the detection of acute myocardial infarction using standard electrocardiogram (ECG) leads.The study of myocardial infarction in early stage (MIES) feature extraction was performed using Frank orthogonal leads.Multivariate autoregressive (MAR) model coefficients were used as ECG features for the classification.The data in the analysis including health control (HC),MIES and AMI were collected from PTB diagnostic ECG database.The experimental results show that it is feasible to separate MIES from AMI using Frank orthogonal leads.The accuracy of detecting MIES and AMT increased effectively compared with that of standard ECG leads using the same feature dimension and threshold value of zero.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2008-05-08;修回日期:2008-12-09
基金项目:浙江省自然科学基金资助项目(Y104284)
作者简介:葛丁飞(1965-),男(汉族),浙江省东阳市人,浙江科技学院副教授.E-mail:gedingfei@vip.163.com
更新日期/Last Update: 2009-05-15