Hybrid efficient compression method for electrocardiogram signal transmission based on discrete wavelet transform and principal component analysis

Hybrid efficient compression method for electrocardiogram signal transmission based on discrete wavelet transform and principal component analysis

<p>In this study, the researcher proposed a new approach for the compression of ECG signals by reducing the ECG data size for storage purposes which could speedily transmit data from the client to the server, preserve important diagnostic information from distortion within compressed s...

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Main Author: Abdulbaqi, Azmi Shawkat
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Language:eng
Published: 2020
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Abdulbaqi, Azmi Shawkat
Hybrid efficient compression method for electrocardiogram signal transmission based on discrete wavelet transform and principal component analysis
description <p>In this study, the researcher proposed a new approach for the compression of ECG signals by reducing the ECG data size for storage purposes which could speedily transmit data from the client to the server, preserve important diagnostic information from distortion within compressed signals, and maintain the quality of the reconstructed signal. This research was based on an experimental design involving two phases. In the first phase, DWT, which is a powerful compression tool, was used to compress ECG signals. In the compression process, PCA transferred the properties of compressed signals to MECG to maintain important cardiac features of a diagnostic area. In addition, this tool was used to reduce data dimensions to achieve optimal compression. In the second phase, the encryption of ECG signals during data transmission was performed to safeguard the privacy of patients. The findings showed that the performances of DWT and PCA algorithms were relatively superior that those of existing algorithms. Specifically, PCA was highly effective in the compression of multichannel ECG data. Likewise, DWT was also effective in the ECG signal compression involving QRS Regions and Non-QRS Regions. Moreover, it was found that ECG signals, including biomedical signals, could be represented in low bits per pixel with good quality. Revealingly, the findings showed that the proposed method managed to attain an average CR of 11.00 %, with PRD is less than 0.66 % and QS is equal to 29.71 %. Overall, these findings suggest that DWT and PCA algorithms can be effectively used for ECG signal monitoring and diagnostic applications.</p>
format thesis
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qualification_level Doctorate
author Abdulbaqi, Azmi Shawkat
author_facet Abdulbaqi, Azmi Shawkat
author_sort Abdulbaqi, Azmi Shawkat
title Hybrid efficient compression method for electrocardiogram signal transmission based on discrete wavelet transform and principal component analysis
title_short Hybrid efficient compression method for electrocardiogram signal transmission based on discrete wavelet transform and principal component analysis
title_full Hybrid efficient compression method for electrocardiogram signal transmission based on discrete wavelet transform and principal component analysis
title_fullStr Hybrid efficient compression method for electrocardiogram signal transmission based on discrete wavelet transform and principal component analysis
title_full_unstemmed Hybrid efficient compression method for electrocardiogram signal transmission based on discrete wavelet transform and principal component analysis
title_sort hybrid efficient compression method for electrocardiogram signal transmission based on discrete wavelet transform and principal component analysis
granting_institution Universiti Pendidikan Sultan Idris
granting_department Fakulti Seni, Komputeran dan Industri Kreatif
publishDate 2020
url https://ir.upsi.edu.my/detailsg.php?det=8744
_version_ 1776104562745147392
spelling oai:ir.upsi.edu.my:87442023-03-07 Hybrid efficient compression method for electrocardiogram signal transmission based on discrete wavelet transform and principal component analysis 2020 Abdulbaqi, Azmi Shawkat <p>In this study, the researcher proposed a new approach for the compression of ECG signals by reducing the ECG data size for storage purposes which could speedily transmit data from the client to the server, preserve important diagnostic information from distortion within compressed signals, and maintain the quality of the reconstructed signal. This research was based on an experimental design involving two phases. In the first phase, DWT, which is a powerful compression tool, was used to compress ECG signals. In the compression process, PCA transferred the properties of compressed signals to MECG to maintain important cardiac features of a diagnostic area. In addition, this tool was used to reduce data dimensions to achieve optimal compression. In the second phase, the encryption of ECG signals during data transmission was performed to safeguard the privacy of patients. The findings showed that the performances of DWT and PCA algorithms were relatively superior that those of existing algorithms. Specifically, PCA was highly effective in the compression of multichannel ECG data. Likewise, DWT was also effective in the ECG signal compression involving QRS Regions and Non-QRS Regions. Moreover, it was found that ECG signals, including biomedical signals, could be represented in low bits per pixel with good quality. Revealingly, the findings showed that the proposed method managed to attain an average CR of 11.00 %, with PRD is less than 0.66 % and QS is equal to 29.71 %. Overall, these findings suggest that DWT and PCA algorithms can be effectively used for ECG signal monitoring and diagnostic applications.</p> 2020 thesis https://ir.upsi.edu.my/detailsg.php?det=8744 https://ir.upsi.edu.my/detailsg.php?det=8744 text eng closedAccess Doctoral Universiti Pendidikan Sultan Idris Fakulti Seni, Komputeran dan Industri Kreatif <p>Abdelmounim, E., Haddadi, R., Belaguid, A., & others. (2014). A new simple and efficient technique for ECG compression based on leads converter and DWT coefficients thresholding. In Complex Systems (WCCS), 2014 Second World Conference on (pp. 638643).</p><p>Abo-Zahhad, M., Ahmed, S. M., & Zakaria, A. (2011). ECG signal compression technique based on discrete wavelet transform and QRS-complex estimation. Signal Processing--An International Journal (SPIJ), 4(2), 138.</p><p>Abo-Zahhad, M., Al-Ajlouni, A. F., Ahmed, S. M., & Schilling, R. J. (2013). 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