[1]张清丽,苏士美#,尹咪咪,等.基于心电特征参数的心肌梗死疾病辅助诊断模型的建立*[J].郑州大学学报(医学版),2017,(02):151-154.[doi:10.13705/j.issn.1671-6825.2017.02.011]
 ZHANG Qingli,SU Shimei,YIN Mimi,et al.Establishment of myocardial infarction disease-diagnosis model based on ECG characteristic parameters[J].JOURNAL OF ZHENGZHOU UNIVERSITY(MEDICAL SCIENCES),2017,(02):151-154.[doi:10.13705/j.issn.1671-6825.2017.02.011]
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基于心电特征参数的心肌梗死疾病辅助诊断模型的建立*()
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《郑州大学学报(医学版)》[ISSN:1671-6825/CN:41-1340/R]

卷:
期数:
2017年02期
页码:
151-154
栏目:
应用研究
出版日期:
2017-03-15

文章信息/Info

Title:
Establishment of myocardial infarction disease-diagnosis model based on ECG characteristic parameters
作者:
张清丽苏士美#尹咪咪张建华刘 莹
郑州大学电气工程学院 郑州 450001
Author(s):
ZHANG QingliSU ShimeiYIN MimiZHANG JianhuaLIU Ying
School of Electrical Engineering,Zhengzhou University, Zhengzhou 450001
关键词:
心肌梗死 小波变换 logistic回归模型 支持向量机模型
Keywords:
myocardial infarction wavelet transform logistic regression model support vector machine model
分类号:
R540.4
DOI:
10.13705/j.issn.1671-6825.2017.02.011
摘要:
目的:建立基于心电特征参数的心肌梗死疾病辅助诊断模型。方法:取PTB数据库中的158例心肌梗死患者为病例组,90例健康志愿者为对照组,提取这2组的ECG V5导联信号波形并进行预处理,用小波变换结合窗口函数的方法提取11个心电特征参数,采用独立样本t检验和精确概率法筛选特征参数,并进行归一化处理,建立logistic回归模型和支持向量机模型并比较其性能。结果:Logistic回归模型和支持向量机模型的诊断准确率分别为95.1%和96.0%。结论:Logistic回归模型和支持向量机模型对心肌梗死的分类诊断均具有重要的理论和临床价值。
Abstract:
Aim: To establish a myocardial infarction disease-diagnosis model based on ECG characteristic parameters.Methods: A total of 158 cases of myocardial infarction(case group)and 90 healthy volunteers(control group)in PTB database were chosen. ECG V5 lead signal waveforms of the 2 groups were extracted and preprocessed, 11 ECG characteristic parameters were extracted using wavelet transform combined with window function,among which, characteristic parameters were selected using independent sample t test and exact probability method, and normalized, and then the mathematical models were established based on logistic regression model and support vector machine(SVM)model, finally,the performance of the 2 models was compared.Results: The diagnostic accuracy of the logistic regression model was 95.1%,and that of the SVM model was 96.0%.Conclusion: Logistic regression model and SVM model are both of great value in classification diagnosis of myocardial infarction.

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备注/Memo

备注/Memo:
#通信作者,女,1965年6月生,硕士,副教授,研究方向:信息的采集与处理,E-mail:smsu@zzu.edu.cn
更新日期/Last Update: 2017-03-20