The aim of this paper is twofold. First, we define an ECG feature parameter set (32 features) which could represent ECG signal as adequately as possible for diagnosing requirements. Second, we design an automatic classification framework. After benchmark point detection, feature parameter will be extracted. And then the classifier methods and its comparison based on SVM and QNN are presented. The long-term objective is to design a thorough system to realize the recognition of real-time ECG signal and enhance medical treatment.
목차
Abstract 1. Introduction 2. Feature Extraction and Selection 2.1. Feature Extraction using Wavelet Transform 2.2. Feature Selection Methods 3. Classifier Design 3.1. SVM Classifier 3.2. QNN Classifier 4. Experiment and Comparison 4.1. Experiment result with BP and RBF Neural Network Classifiers 4.2. Experiment Result with SVM Classifier 4.3. Experiment Result with QNN Classifier 5. Conclusion and Expectation Acknowledgements References
Xiao Tang [ School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China, College of Mathematics and Software Science, Sichuan Normal University, Chengdu, 610066, China ]
Shu Lan [ College of Mathematics and Software Science, Sichuan Normal University, Chengdu, 610066, China ]
보안공학연구지원센터(IJSIP) [Science & Engineering Research Support Center, Republic of Korea(IJSIP)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Signal Processing, Image Processing and Pattern Recognition
간기
격월간
pISSN
2005-4254
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.7 No.2