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: | |
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| Format: | thesis |
| Language: | eng |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://ir.upsi.edu.my/detailsg.php?det=8744 |
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| Summary: | <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> |
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