Earticle

초록

영어

In this paper a novel quick automatic method is proposed for electrocardiogram (ECG). Signal classification to three classes include: the normal heart beats from the left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. After noise reduction using wavelet threshold, appropriate features are extracted from the time-voltage waves including P, Q, S, and T waves in ECG signals. Novelty of this work is utilization of fast decision based on non-parametric statistical classifier and Multi Features Data Fusion (MFDF) strategy. Two stages of MFDF include feature classification into normal and abnormal categories. Based on decision template, first stage, and second part are voting and weighting the procedure. Post processing block is added for impulsive noise reduction in order to improve the results. We emphasized on the performance and efficiency of the optimized presented algorithm and minimum cost of system learning. The accuracy of final results is reliable and well performed.

목차

Abstract
 1. Introduction
 2. Preliminaries
  2.1 Wavelet Transform Thresholding
  2.2 Otsu Thresholding Method
  2.3 Multi Sensor Data Fusion
 3. The Proposed
  3.1. Data Set Description
  3.2. Noise Reduction
  3.3. Feature Extraction
  3.4 Feature Thresholding
  3.5 Voting
  3.6 Feature Selection
  3.7 Making Decision Template
 4. Experimental Result
 5. Conclusion
 References

키워드

저자

• Ali Abharya [ Department of Computer Engineering, Ferdowsi University of Mashhad ]
• Shahram Shahidi [ Department of Computer Engineering, Ferdowsi University of Mashhad ]
• H. SadoghiYazdi [ Department of Computer Engineering, Ferdowsi University of Mashhad, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad ]

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