International Journal of Electrical, Electronics and Data Communication (IJEEDC)
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Volume-7,Issue-12  ( Dec, 2019 )
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Statistics report
Feb. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 84
Paper Published : 1397
No. of Authors : 3784
  Journal Paper

Paper Title
Integrated Approach in QRS Complex Analysis of ECG using Wavelet Transform

Abstract
Over the last decades, a lot of progress have been made in QRS complex analysis. However, up to date analysis have been done visualy without any software due to the high cost of equipment which make it challenging to detect abrupt and abnormal changes which may be a sign of abnormal fonction of the heart. From the 80’s with the work of different researcher’s wavelet transform has found many applications in Engineering and Science which includes medical imaging, with continuous wavelet transform being used to solve the limitation of the Fourier transform to build a time-frequency representation of a signal that gives great frequency and time localization as an example. In this paper, we aim to show the applicability of wavelet transform to run QRS analysis to detect abnormal change which can be detected with naked eyes. We have run multiple analysis with different set of Electrocardiogram of patient with known and unknown cardiac problems using different types of integration kernels. The purpose of this work is to give a better understanding of the wavelet application in QRS complex analysis and to demonstrate how it can be applied to define identify the different problems that the heart may be subject to. We have collected different sets printed electrocardiogram and digitalize them using commercial software, a simpler approach would be to collect the ECG directly from the machine. After preparation of the data, we have used Mexican hat, Meyer, Haar, Daubechies, coiflets, symlets and morlet to analysis the different logs and we have compare the different scalograms. Morlet wavelet used on ECG from different patients has shown a great resolution on the identification of QRS abnormalities. While the gaus level 4 have demonstrated a good visual on detecting depolarization due to muscular contraction, Morlet shows even better resolution.


Author - Justine Kasay Bwenge

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| Published on 2019-06-03
   
   
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