Wavelet-Based Multiscale Sample Entropy and Chaotic Features for Congestive Heart Failure Recognition Using Heart Rate Variability
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This study explores the discrimination power of a multiscale analysis method based on the discrete wavelet transform (DWT) in characterizing nonlinear features for congestive heart failure (CHF) recognition. Two DWT paradigms, namely standard DWT and DWT with reconstruction (RDWT), were employed to characterize two categories of nonlinear features, namely sample entropy (SE) and chaotic features, for CHF recognition based on heart rate variability (HRV). The performance of the wavelet-based analysis methods was compared to that of a traditional coarse grained average (CGA) method. The support vector machine was used as a classifier and the capability of the features was evaluated using the leave-one-out cross-validation method. The results show that when using solely SE features, all three multiscale analysis methods (CGA, DWT, and RDWT) with five dyadic scales outperform traditional CGA with twenty consecutive scales in characterizing HRV for CHF recognition. When using chaotic features calculated from the five dyadic scales, RDWT outperformed DWT and CGA with sensitivity, specificity, and accuracy rates of 95.45, 97.22, and 96.55 %, respectively. This performance was even superior to that obtained using both SE and chaotic features. The proposed multiscale analysis method using 5-scale RDWT and chaotic features outperforms three well-known CHF classifiers reported in the literature.
KeywordsMultiscale Discrete wavelet transform Sample entropy Chaotic features Congestive heart failure Heart rate variability
This study was supported in part by Grants NSC 100-2221-E-194-063 and NSC 101-2221-E-194-019 from the National Science Council, Taiwan.
- 2.Asyali, M. H. (2003) Discrimination power of long-term heart rate variability measures. In Proceedings of the 25th Annual International Conference of the IEEE EMBS (pp. 200–203). Cancum, Mexico.Google Scholar
- 10.Mallat, S. (2009). A wavelet tour of signal processing: The sparse way (3rd ed.). Burlington: Elsevier Inc.Google Scholar
- 11.Lee, M.-Y. and Yu, S.-N. (2012) Multiscale sample entropy based on discrete wavelet transform for clinical heart rate variability recognition. In Proceedings of the 34th Annual International Conference of the IEEE EMBS, San Diego, CA, USA (pp. 4299–4302).Google Scholar
- 12.Richmann, J. S., & Moorman, J. R. (2000). Physiological time series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278, H2039–H2049.Google Scholar
- 15.Kheder, G., Taleb, R., Kachouri, A., Massoued, M. B. and Samet, M. (2009) Feature extraction by wavelet transforms to analyze the heart rate variability during two meditation technique, Chapter 32. In Advances in Numerical Methods, Lecture Notes in Electrical Engineering, New York: Springer.Google Scholar
- 16.McClellan, J. H., Schafer, R. W., & Yoder, M. A. (2003). Signal processing first, Upper Saddle River. New Jersey: Pearson Prentice Hall.Google Scholar
- 21.Takens, F. (1981). Detecting strange attractors in turbulence in dynamical system and turbulence. Berlin: Springer.Google Scholar
- 28.Otani, M. and Jones,A. (2000) Automated embedding and the creep phenomenon in chaotic time series. http://users.cs.cf.ac.uk/Antonia.J.Jones/UnpublishedPapers/Creep.pdf.
- 31.Physiobank. Available: http://www.physionet.org/physiobank/database.
- 33.Lee, M.-Y. and Yu,S.-N. (2010) Improving discriminality in heart rate variability analysis using simple artifact and trend removal preprocessors. In Proceedings of the 32nd Annual International Conference of the IEEE EMBS (pp. 4574–4577). Buenos Aires, Argentina.Google Scholar
- 41.Chou, Y., Zhang, A., Wang, P. & Gu, J. (2014). Pulse rate variability estimation method based on sliding window iterative DFT and Hilbert transform. Journal of Medical and Biological Engineering, 34, 347–355.Google Scholar