Earticle

초록

영어

In the process of ventricular premature beat (PVC) and normal sinus rhythm (NSR) identification base on electrocardiogram (ECG), there exists problems like negative effect from ECG rhythm and low recognition rate. This paper proposes the electrocardiogram PVC classification algorithm based on support vector machine (SVM) and wavelet algorithm. The algorithm uses the wavelet transform to analyze ECG beating model, which is not influenced by the change of ECG waveform. The two feature sets respectively compose of statistical parameters of the wavelet coefficients and the selected wavelet coefficients. PVC and NSR are analyzed by using SVM. The experimental results show that this method improves the recognition rate of ECG.

목차

Abstract
 1. Introduction
 2. ECG research Methods
  2.1. ECG Filter and Heart Beat Detection
  2.2. ECG Feature Extraction
 3. The Basic Theory of SVM
  3.1. Discriminant Function
  3.2. Fisher Linear Discriminant Method
  3.3. Optimal Classification Plane
  3.4. Support Vector Machine
  3.5. Selection of Kernel Function
 4. PVC Classification
 5. Experimental Analysis
 6. Result Analysis
 7. Conclusion
 Acknowledgements
 References

키워드

저자

• Huang Dong [ College of Mathematics and Computers Science, QinZhou University, QinZhou, China, 530099 ]
• Liao Zhengquan [ Internal Medicine-Cardiovascular Department, First People's Hospital of Qinzhou, QinZhou, Guangxi, China, 535000 ]
• Li Changbin [ College of Mathematics and Computers Science, QinZhou University, QinZhou, China, 530099 ]
• Li Dan [ College of Mathematics and Computers Science, QinZhou University, QinZhou, China, 530099 ]
• Huang Wendong [ College of Mathematics and Computers Science, QinZhou University, QinZhou, China, 530099 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605