Earticle

초록

영어

A compression algorithm based on Empirical Mode Decomposition (EMD) is described in order to investigate the performance of EMD in biomedical signals, and especially in the case of electrocardiogram (ECG). The proposed algorithm is computationally simple to treat non-stationary and nonlinear data without pre- or post-processing. In order to evaluate the performance of the proposed compression algorithm, MIT-BIH arrhythmia database is applied, and the compress ratio (CR), percent root mean square difference (PRD), percent root mean square difference normalized (PRDN), root mean square (RMS), signal to noise ratio (SNR), and quality score (QS) values are obtained. When compared, good fidelity parameters are yielded with high CR as compared to wavelet transform (WT).

목차

Abstract
 1. Introduction
 2. Material and Methods
  2.1. Overview of the Proposed Algorithm
  2.2. Empirical Mode Decomposition
  2.3. Peak Detecting of IMFs and Threshold Selecting
  2.4. Huffman Coding
  2.5. Reconstruction of the Compressed ECG Data
 3. Results and Discussion
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Dan Yang [ School of Information Science and Engineering, Northeastern University, Shenyang 110819, China ]
• Meng-zhi Qin [ College of Electronic Science and Applied Physics, Hefei University of Technology, Heifei 230009, Chnia, ] corresponding author
• Bin Xu [ School of Information Science and Engineering, Northeastern University, Shenyang 110819, China ]

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