A Study On The Effectiveness Of Various Wavelet Families In ECG Signal Analysis
DOI:
https://doi.org/10.29284/1vwxb905Keywords:
ECG analysis, wavelet transform, wavelet families, feature extraction, noise reduction, arrhythmia detection, Physionet [7]Abstract
Electrocardiogram (ECG) analysis is a crucial diagnostic method for identifying cardiovascular diseases, as it provides detailed insights into the heart's electrical activity. Among the techniques for analyzing ECG signals, wavelet transforms have proven to be particularly effective due to their ability to offer multi-resolution analysis, capturing both high-frequency details and low-frequency trends. This study undertakes a comparative evaluation of various wavelet families, namely Daubechies, Symlets, Coiflets, and Biorthogonal wavelets, to assess their performance in key areas such as feature extraction, noise reduction, and arrhythmia detection. The findings reveal that Daubechies wavelets excel in general noise reduction and feature extraction, particularly in detecting abrupt changes, making them suitable for identifying critical ECG components. Symlets, with their enhanced symmetry and orthogonality, are effective in preserving signal morphology while providing balanced noise reduction and precise QRS complex detection. Coiflets, noted for their high vanishing moments and near symmetry, offer superior detailed feature extraction and arrhythmia detection capabilities. Biorthogonal wavelets stand out for their ability to ensure exact signal reconstruction, making them ideal for applications requiring perfect signal replication. The study's results provide valuable insights into the strengths and limitations of each wavelet family, guiding their optimal application in various ECG signal processing tasks for accurate cardiac health monitoring and diagnosis. [1], [5], [7]
References
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[4] L. Sörnmo and P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications. Academic Press, 2005.
[5] G. Strang and T. Nguyen, Wavelets and Filter Banks. Wellesley-Cambridge Press, 1996.
[6] M. Blanco-Velasco, B. Weng, and K. E. Barner, "ECG signal denoising and baseline wander correction based on the empirical mode decomposition," Computers in Biology and Medicine, vol. 38, no. 1, pp. 1-13, Jan. 2008.
[7] A. Subasi, "Signal Classification Using Wavelet Transform and Discriminant Analysis," Proceedings of the 5th WSEAS International Conference on Telecommunications and Informatics, Istanbul, Turkey, 2006, pp. 200-204.
[8] MIT-BIH Arrhythmia Database, [Online]. Available: https://physionet.org/physiobank/database/mitdb/. [Accessed: 18-Jul-2022].
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
