Abstract
Multiscale entropy (MSE) is one of the popular techniques to calculate and describe the complexity of the physiological signal. Many studies use this approach to detect changes in the physiological conditions in the human body. However, MSE results are easily affected by noise and trends, leading to incorrect estimation of MSE values. In this paper, singular value decomposition (SVD) is adopted to replace MSE to extract the features of physiological signals, and adopt the support vector machine (SVM) to classify the different physiological states. A test data set based on the PhysioNet website was used, and the classification results showed that using SVD to extract features of the physiological signal could attain a classification accuracy rate of 89.157%, which is higher than that using the MSE value (71.084%). The results show the proposed analysis procedure is effective and appropriate for distinguishing different physiological states. This promising result could be used as a reference for doctors in diagnosis of congestive heart failure (CHF) disease.





Similar content being viewed by others
References
Costa, M., Goldberger, A. L., Peng, C. K., Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Let. 89(6):068102(1–4), 2002.
Ferrario, M., Signorini, M. G., Magenes, G., and Cerutti, S., Comparison of entropy-based regularity estimators: Application to the fetal heart rate signal for the identification of fetal distress. IEEE Trans Biomed Eng 53(1):119–125, 2006.
Patrick, R. N., Anderson, S. M., Jenkins, J. M., Williams, A. E., and Morris, J. A., Jr., Heart rate multiscale entropy at three hours predicts hospital mortality in 3,154 trauma patient. Shock 30(1):17–22, 2008.
Hung, C. H., and Jiang, B. C., Multi-scale entropy approach to physiological fatigue during long-term web browsing. Hum Factors Ergon Manuf 19(5):478–493, 2009.
Trunkvalterova, Z., Javorka, M., Tonhajzerova, I., Javorkova, J., Lazarova, Z., Javorka, K., and Baumert, M., Reduced short-term complexity of heart rate and blood pressure dynamic in patients with diabetes mellitus type 1: Multiscale entropy analysis. Physiol Meas 29:817–828, 2008.
Park, J. H., Kim, S., Kim, C. H., Cichocki, A., and Kim, K., Multiscale entropy analysis of EEG from patients under different pathological conditions. Fractals 15(4):399–404, 2007.
Costa, M., Peng, C. K., Goldberger, A. L., and Hausdorff, J. M., Multiscale entropy analysis of human gait dynamics. Physica. A, Statistical mechanics and its applications 330(1–2):53–60, 2003.
Valencia, J. F., Porta, A., Vallverdú, M., Clarià, F., Baranowski, R., Orłowska-Baranowska, E., and Caminal, P., Refined multiscale entropy: Application to 24-h holter recordings of heart period variability in healthy and aortic stenosis subjects. IEEE Trans Biomed Eng 45(9):2202–2213, 2009.
McNames J., Thong T., Aboy M. Impulse rejection filter for artifact removal in spectral analysis of biomedical signals. Proceedings of the 26th Annual International Conference of the IEEE EMBS, 145–148, September 1–5 2004
Kanjilal, P. P., Palit, S., and Saha, G., Fetal ECG extraction from single-channel maternal ECG using singular value decomposition. IEEE Trans Biomed Eng 44(1):51–59, 1997.
Ayat, M., Assaleh, K., and Nashash, H., Fetal ECG extraction from a single abdominal ECG signal using SVD and polynomial classifiers. IEEE Workshop on Machine Learning for Signal Processing, Cancun, Mexico, 2008.
Moor, B. D., The singular value decomposition and long and short spaces of noisy matrices. IEEE Trans Signal Process 41(9):2826–2838, 1993.
Physiobank Archive Index, Normal Sinus Rhythm RR Interval Database: http://www.physionet.org/physiobank/database/nsr2db (Access time: 28.10.2009).
Physiobank Archive Index, Congestive Heart Failure RR Interval Database: http://www.physionet.org/physiobank/database/chf2db (Access time: 28.10.2009).
Huang, N. E., Wu, M. C., Long, S. R., Shen, S. S. P., Qu, W., Gloersen, P., and Fan, K. L., A confidence limit for the empirical mode decomposition and hilbert spectrum analysis. Proc R Soc Lond A 459:2317–2345, 2003.
Acknowledgment
This research was supported by the National Science Council of Taiwan (No: NSC 97-2221-E-155-048-MY3). The authors would like to thank Ary L. Goldberger and C. K. Peng at Harvard Medical School for their valuable suggestions.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chang, CD., Wang, CC. & Jiang, B.C. Singular Value Decomposition Based Feature Extraction Technique for Physiological Signal Analysis. J Med Syst 36, 1769–1777 (2012). https://doi.org/10.1007/s10916-010-9636-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10916-010-9636-3