[1]李润川,张行进,陈刚,等.基于多特征融合的心搏类型识别研究[J].郑州大学学报(工学版),2021,42(04):7-12.[doi:10.13705/j.issn.1671-6833.2021.04.011]
　Li Runchuan,Zhang Xingjin,Chen Gang,et al.Research on Heartbeat Type Recognition Based on Multi-feature Fusion[J].Journal of Zhengzhou University (Engineering Science),2021,42(04):7-12.[doi:10.13705/j.issn.1671-6833.2021.04.011]

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基于多特征融合的心搏类型识别研究()

分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

42

期数:

2021年04期

页码:

7-12

栏目:

出版日期:

2021-07-30

文章信息/Info

Title:

Research on Heartbeat Type Recognition Based on Multi-feature Fusion

作者:

李润川; 张行进; 陈刚; 姚金良; 于捷; 王宗敏

郑州大学信息工程学院;郑州大学互联网医疗与健康服务河南省协同创新中心;

Author(s):

Li Runchuan; Zhang Xingjin; Chen Gang; Yao Jinliang; Yu Jie; Wang Zongmin;

School of Information Engineering, Zhengzhou University; Zhengzhou University Internet Medical and Health Services Henan Coordinated Innovation Center;

关键词:

心电图; 多特征融合; KNN模型; 心搏分类

Keywords:

ECG; multi-feature fusion; KNN model; heartbeat classification

DOI:

10.13705/j.issn.1671-6833.2021.04.011

文献标志码:

A

摘要:

心律失常是一种常见的心电活动异常，严重的可能会危及人的生命，因此准确诊断心律失常具有十分重要的意义。本文提出了一种新的方法，用于心律失常诊断中对心搏的识别分类。首先对原始心电信号进行去噪预处理，并根据R峰位置获得心搏段。然后提取235单心博特征点、R波幅值、PR间期、QT间期、ST段和RR间期作为特征参数，并对比分析不同特征组合下分类的性能，最后基于KNN模型使用最佳特征组合将心搏分为三类。实验结果表明，本文提出的基于多特征融合与KNN模型的心搏分类方法相比于其他方法具有更好的性能。

Abstract:

Arrhythmia is a common abnormality of cardiac electrical activity,which may seriously endanger human life.Therefore,in order to accurately diagnose arrhythmia,this paper presents a new method for the recognition and classification of heartbeat in the diagnosis of arrhythmia.This paper proposed a new method for the recognition and classification of heartbeat in the diagnosis of arrhythmia.Firstly,the original ECG signal was denoised and preprocessed,and the heartbeat segment was obtained according to the R peak position.Then 235 single heartbeat feature points,R wave amplitude,PR interval,QT interval,ST segment and RR interval as feature parameters,and the performance of classification under different feature combinations were comparatively analyzed to select the best feature combination.Finally,the KNN model was used to classify the heartbeat based on the best feature combination.In this paper,experiments on MIT-BIH arrhythmia database,and according to ANSI/AAMI classification,they were classified three types of heart beats:normal or bundle branch block (N),supraventricular ectopic beat (S),and ventricular ectopic beat (V).The results showed that the sensitivity and positive predictive value of S type heart beats were 87.8% and 95.1%,respectively.The sensitivity and positive predictive value of V type heart beats were 96.6% and 98.2%,respectively.The average accuracy of measurement was 99.2%.Compared with other cardiac classification methods,the proposed cardiac classification method based on multi-feature fusion and KNN model could improve the classification accuracy,with higher sensitivity and positive predictive value,and it was of great significance for clinical decision-making.

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