| 基于改进熵特征的心律失常分类研究 |
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| 引用本文： | 田长平,张长胜,赵振刚,张家洪,陈玮,彭玮,李川,李英娜. 基于改进熵特征的心律失常分类研究[J]. 光电子．激光, 2021, 32(12): 1338-1344 |
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| 作者姓名： | 田长平 张长胜 赵振刚 张家洪 陈玮 彭玮 李川 李英娜 |
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| 作者单位： | 昆明理工大学信息工程与自动化学院,云南昆明650500;杭州柏医健康科技有限公司,浙江杭州311200 |
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| 基金项目： | 国家自然科学基金(61972185)资助项目 (1.昆明理工大学 信息工程与自动化学院,云南 昆明 650500； 2.杭州柏医健康科技有限公司,浙江 杭州 311200) |
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| 摘 要： | 心律失常类型的判断对心血管疾病的防治十分重要,针对波动散布熵(multiscale fluctuation dispersion entropy,FDE)在进行心律失常分类识别时尺度单一、不能全面反映心律失常信息等不足,通过改进FDE特征,提出一种基于自适应多尺度波动散布熵(adaptive multiscale fl...
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| 关 键 词： | 心律失常分类 变分模态分解 AMFDE GA-SVM |
| 收稿时间： | 2021-04-09 |
Research on classification of arrhythmia based on improved entropy feature |
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| TIAN Changping,ZHANG Changsheng,ZHAO Zhengang,ZHANG Jiahong,CHEN Wei,PENG Wei,LI Chuan and LI Yingna. Research on classification of arrhythmia based on improved entropy feature[J]. Journal of Optoelectronics·laser, 2021, 32(12): 1338-1344 |
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| Authors: | TIAN Changping ZHANG Changsheng ZHAO Zhengang ZHANG Jiahong CHEN Wei PENG Wei LI Chuan LI Yingna |
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| Affiliation: | Faculty of Information Engineering and Automation,Kunming University of Scien ce and Technology,Kunming,Yunnan 650500,China,Faculty of Information Engineering and Automation,Kunming University of Scien ce and Technology,Kunming,Yunnan 650500,China,Faculty of Information Engineering and Automation,Kunming University of Scien ce and Technology,Kunming,Yunnan 650500,China,Faculty of Information Engineering and Automation,Kunming University of Scien ce and Technology,Kunming,Yunnan 650500,China,Hangzhou Baiyi Health Technolog y Co.,Ltd.Hangzhou,Zhejiang 311200,China,Faculty of Information Engineering and Automation,Kunming University of Scien ce and Technology,Kunming,Yunnan 650500,China,Faculty of Information Engineering and Automation,Kunming University of Scien ce and Technology,Kunming,Yunnan 650500,China and Faculty of Information Engineering and Automation,Kunming University of Scien ce and Technology,Kunming,Yunnan 650500,China |
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| Abstract: | The judgment of the type of arrhythmia is very important for the preven tion and treatment of cardiovascular diseases.In view of the shortcomings of multiscale fluctuation d ispersion entropy (FDE) in arrhythmia classification and recognition,such as single scale,unable to fully reflect the arrhythmia information and so on.By improving FDE features,An arrhythmia classification method based on adaptive multiscale fluctuation dispersion entropy (AMFDE) is proposed.Firstly,the signal is decom posed by using the variable mode decomposition (VMD) of the optimized K value before the FDE features are calculated,and then FDE of each scale subsequence is extracted as classification feature,and genetic algor ithm (GA) is used The support vector machine (SVM) penalty factor c and kernel funct ion parameter g are optimized,and finally the GA-SVM model is used for pattern recognition.Through the experimental verification,t he average accuracy,sensitivity and specificity of the proposed method for four types of heart rhythm are 95.3%, 95.4% and 98.4%,respectively. Compared with other methods,such as AMDE-SVM,it has obvious advantages,the pr oposed method has obvious advantages and can achieve accurate classification of electrocardiogram (ECG) signals. |
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| Keywords: | arrhythmia classification variational modal decomposition fluctuation dispersio n entropy GA-SVM |
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