원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.8 No.1
2015.01
pp.193-202
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보