Research on Heartbeat Type Recognition Based on Multi-feature Fusion
[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
42卷
Number of periods:
2021 04
Page number:
7-12
Column:
Public date:
2021-07-30
- Title:
- Research on Heartbeat Type Recognition Based on Multi-feature Fusion
- Keywords:
- ECG; multi-feature fusion; KNN model; heartbeat classification
- CLC:
- -
- DOI:
- 10.13705/j.issn.1671-6833.2021.04.011
- 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.
- References:
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[1] 逯鹏,李奇航,尚莉伽,等.基于优化极限学习机的CVD预测模型研究[J].郑州大学学报(工学版),2019,40(2):1-5.[2] ZHU W L,CHEN X H,WANG Y,et al.Arrhythmia recognition and classification using ECG morphology and segment feature analysis[J].IEEE/ACM transactions on computational biology and bioinformatics,2019,16(1):131-138.
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Last Update:
2021-08-26