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Piecewise Modeling of ECG Signals Using Chebyshev Polynomials

  • Om Prakash YadavEmail author
  • Shashwati Ray
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)

Abstract

An electrocardiogram (ECG) signal measures electrical activity of the heart which is used for cardiac-related issues. The morphology of these signals is affected by artifacts during acquisition and transmission which prevents accurate diagnosis. Also a typical ECG monitoring device generates massive volume of digital data which require huge memory and large bandwidth. So there is a need to effectively compress these signals. In this paper, a piecewise efficient model to compress ECG signals is proposed. The model is designed to perform three successive steps: denoising, segmentation, and approximation. Preprocessing is done through total variation denoising technique to reduce noise, while bottom-up time-series approach is implemented to divide the signals into various segments. The individual segments are then approximated using Chebyshev polynomials. The proposed model is compared with other compression models in terms of maximum error, root mean square error, percentage root mean difference, and normalized percentage root mean difference showing significant improvements in performance parameters.

Keywords

ECG Total variation denoising Bottom-Up approach Chebyshev approximation 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Bhilai Institute of Technology DurgChhattisgarhIndia

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