Impact of Partial Update on Denoising Algorithms of ECG Signals

Authors

  • Ashraf A.M. Khalaf Department of Electronics & Communications Engineering, Faculty of Engineering, Minia University, Minia, Egypt.
  • Ashraf M. Said Department of Biomedical Engineering, Faculty of Engineering, Minia University, Minia, Egypt.
  • M.M. Ibrahim Department of Electronics & Communications Engineering, Faculty of Engineering, Minia University, Minia, Egypt.
  • H.F. A. Hamed Department of Electronics & Communications Engineering, Faculty of Engineering, Minia University, Minia, Egypt.

Keywords:

Partial Update, ECG Noise Canceller, Neural Network, Adaptive Filters, Mean Square Error,

Abstract

This work aims to propose and study the effects of partial update procedure on various ECG denoising algorithms. The partial update algorithms are applied to overcome different types of noises such as Power-Line Interference (PLI), Baseline Wander (BW), Electrode Motion artifacts (EM) and Muscle Artifacts (MA). The impact of partial update (PU) on multiple algorithms and spatially adaptive filters and multi-layer Neural Network (NN) are studied and demonstrated. The performance of different algorithms are evaluated by measuring the Signalto-Noise Ratio after cancellation (Post-SNR), the Mean Square Error (MSE) and the Percent Root Mean Square Difference (PRD%).

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Published

2018-02-15

How to Cite

Khalaf, A. A., Said, A. M., Ibrahim, M., & Hamed, H. A. (2018). Impact of Partial Update on Denoising Algorithms of ECG Signals. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-8), 129–134. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3764

Issue

Vol. 10 No. 1-8: Breakthrough To Excellence in Communication and Computer Engineering IV

Section

Articles

License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)