Earticle

초록

영어

In this paper a novel unsupervised classification method for electrocardiogram (ECG) signal classification is presented. The proposed approach classifies the input signal into normal and abnormal heartbeat patterns with a relatively high accuracy. After extracting features from the time-voltage waves in ECG signals, we utilize a computationally fast algorithm based on log likelihood strategy for change detection on selected features. We then combine the outputs based on their validation coefficient. The Algorithm could differentiate between the normal and unknown heart features. Experimental results show the accuracy of the proposed approach in terms of reliability and performance.

목차

Abstract
 1. Introduction
 2. Preliminaries
  2.1. Generalized Likelihood Ratio (GLR) Change Detector
  2.2 Shannon Entropy
 3. The Proposed Method
 4. Discussion
 5. Experimental Result
 6. Conclusion
 References

키워드

저자

• M. Ali Majidi [ Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran ]
• H. SadoghiYazdi [ Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Iran ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605