Abstract
This paper explores an approach to study entropy differentiations of heart’s activities estimation in Low Frequency (LF) and High Frequency (HF) bands. Dataset composed of 34 ECGs, obtained from healthy and diabetic rats under normal and exercise living conditions. RR intervals extracted efficiently in order to create Heart Rate (HR) time series. Continuous Wavelet Transform (CWT) has been used, as the most appropriate approach, to evaluate the effects of exercise on healthy and diabetic HR variability (HRV). Statistical analysis performed taking into account both wavelet entropy in the low and the high frequency selected bands and the corresponding index LF/HF of the wavelet coefficients. Our results show that wavelet entropy measure based on CWT decomposition can capture significant differences between the specific frequency regions that are intrinsically related to the structure of the RR signal. According to our analysis, diabetic rats living under exercise conditions appear to have a reduced LF/HF entropy ratio compared to healthy population.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akay, M., Mello, C.: Wavelets for biomedical signal processing. In: 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 6, pp. 2688–2691 (1997)
Stein, P.K., Kleiger, R.E.: Insights from the study of heart rate variability. Annual Review of Medicine 50, 249–261 (1999)
King, H., Aubert, R.E., Herman, W.H.: Global burden of diabetes, 1995–2025: prevalence, numerical estimates, and projections. Diabetes Care 21, 1414–1431 (1998)
Tuomilehto, J., Borch-Johnsen, K., Molarius, A., Forsen, T., Rastenyte, D., Sarti, C., Reunanen, A.: Incidence of cardiovascular disease in Type 1 (insulin-dependent) diabetic subjects with and with-out diabetic nephropathy in Finland. Diabetologia 41, 784–790 (1998)
Helmrich, S.P., Ragland, D.R., Leung, R.W., Paffenbarger, R.S.: Physical activity and reduced occurrence of non-Insulin-dependent diabetes Mellitus. New England Journal of Medicine 325, 147–152 (1991)
Couderc, J.P., Zareba, W.: Contributions of wavelets to non-invasive electro-cardiology. Ann. Noninvasive Electrocardiol. 3, 54–62 (1998)
Gramatikov, B., Thakor, N.: Wavelet analysis of coronary artery occlusion related changes in ECG. In: 15th Intern. Annual Conf. IEEE/EMBS, San Diego, p. 731 (1993)
Nagendra, H., Mukherjee, S., Vinod, K.: Application of wavelet techniques in ECG Signal processing: An overview. International Journal of Engineering Science and Technology 3, 7432–7443 (2011)
Jeong, Y.H., Su, K.L., Hong, B.P.: Denoising ECG using translation invariant multiwavelet. International Journal of Electrical and Electronics Engineering 3, 138–142 (2009)
Arvinti, B., Costache, M., Toader, D., Oltean, M., Isar, A.: ECG statistical denoising in the wavelet domain. In: 9th IEEE International Symposium of Electronics and Telecommunications, ISETC, Timisoara, pp. 307–310 (2010)
Quain, Q.R., Rosso, O.A., Basar, E., Schurmann, E.: Wavelet entropy in event-related potentials: a new method shows ordering of EEG oscillations. Biol. Cybern. 84, 291–299 (2001)
Bunluechokchai, S., English, M.J.: Detection of wavelet transform-processed ventricular late potentials and approximate entropy. IEEE Computers in Cardiology, 549–552 (2003)
Figliola, F., Serrano, E.: Analysis of physiological time series using wavelet transforms. IEEE Eng. Med. and Bio. Mag., 74–79 (1997)
Mallet, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Pattern Anal. and Machine Intell. 11 (1989)
Kadambe, S., Murray, R., Boudreaux-Bartels, G.F.: Wavelet transform - based QRS complex detector. IEEE Transactions on Biomedical Engineering 46, 838–848 (1999)
Li, C., Zheng, C., Tai, C.: Detection of ECG characteristic points using wavelet transforms. IEEE Transactions on Biomedical Engineering 42, 21–28 (1995)
Pichot, V., Roche, F., Gaspoz, J.M., Enjolras, F., Antoniadis, A., Minini, P., Costes, F., Busso, T., Lacour, J.R., Barthelemy, J.C.: Relation between heart rate variability and training load in middle-distance runners. Medicine and Science in Sports and Exercise 32, 1729–1736 (2000)
Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inform. Theory 36, 961–1005 (1990)
Oikonomidis, D.L., Tsalikakis, D.G., Baltogiannis, G.G., Tzallas, A.T., Xourgia, X., Agelaki, M.G., Megalou, A.J., Fotopoulos, A., Papalois, A., Kyriakides, Z.S., Kolettis, T.M.: Endothelin-B receptors and ventricular arrhythmogenesis in the rat model of acute myocardial infarction. Basic Research in Cardiology 2, 235–245 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Tsalikakis, D.G., Nakos, I., Tzallas, A.T., Karvounis, E., Tsipouras, M. (2013). Effects of Exercise in Diabetic Rats Using Continuous Wavelet Transform. In: Angelis, C.T., Fotiadis, D., Tzallas, A.T. (eds) Ambient Media and Systems. AMBI-SYS 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 118. Springer, Cham. https://doi.org/10.1007/978-3-319-04102-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-04102-5_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04101-8
Online ISBN: 978-3-319-04102-5
eBook Packages: Computer ScienceComputer Science (R0)