Abstract
So far, the pulse rate variability (PRV) analysis methods cannot effectively extract the nonlinear changes of heart beat and need long time data. So a non-linear approach, sign series entropy analysis (SSEA), is employed to derive age-related alterations from short-term PRV, and a probabilistic neural network (PNN) is designed to classify subjects according to their ages. Continuous non-invasive blood pressure signals are chosen to generate short-term PRV signals as the experimental data, and their time domain and frequency domain parameters are also extracted for comparison. The experimental results show that the sign series entropy has a significant difference between young and old subjects, even if the PRV is corrupted by heavy noises; and PNN can accurately classify subjects. SSEA is more suitable for analyzing short-term PRV signals.
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
References
Thayer, J.F., Yamamoto, S.S., Brosschot, J.F.: The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. Int. J. Cardiol. 141, 122–131 (2010)
Jang, D.G., Farooq, U., Park, S.H., Hahn, M.: A robust method for pulse peak determination in a digital volume pulse waveform with a wandering baseline. IEEE Trans. Biomed. Circuits Syst. 8(5), 729–737 (2014)
Heathers, J.A.: Smartphone-enabled pulse rate variability: an alternative methodology for the collection of heart rate variability in psychophysiological research. Int. J. Psychophysiol. 89(3), 297–304 (2013)
Schäfer, A., Vagedes, J.: How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. Int. J. Cardiol. 166(1), 15–29 (2013)
Mohan, A., James, F., Fazil, S., Joseph, P.K.: Design and development of a heart rate variability analyzer. J. Med. Syst. 36(3), 1365–1371 (2012)
Takahashi, A.C., Porta, A., Melo, R.C., Quitério, R.J., Silva, E.D., Borghi, S.A., Tobaldini, E., Montano, N., Catai, A.M.: Aging reduces complexity of heart rate variability assessed by conditional entropy and symbolic analysis. Intern. Emerg. Med. 7(3), 229–235 (2012)
Bian, C., Ma, Q., Si, J., Wu, X., Shao, J., Ning, X., Wang, D.: Sign series entropy analysis of short-term heart rate variability. Chin. Sci. Bull. 54(24), 4610–4615 (2009)
The MIMIC Database. http://www.physionet.org/cgi-bin/atm/ATM
Chou, Y.X., Zhang, A.H., Wang, P., Gu, J.: Pulse rate variability estimation method based on sliding window iterative DFT and Hilbert transform. J. Med. Biol. Eng. 34(4), 347–355 (2014)
Wang, J.S., Chiang, W.C., Hsu, Y.L., Yang, Y.T.: ECG arrhythmia classification using a probabilistic neural network with a feature reduction method. Neurocomputing 116, 38–45 (2013)
Acknowledgments
This work was supported by the open project program of the national laboratory of pattern recognition (grant 201407347), the natural science foundation of Gansu province (grant 1308RJZA225, 145RJ2A065) and the national natural science foundation of China (grant 81360229).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Chou, Y., Zhang, A. (2015). Age-Related Alterations in the Sign Series Entropy of Short-Term Pulse Rate Variability. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_76
Download citation
DOI: https://doi.org/10.1007/978-3-319-22053-6_76
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22052-9
Online ISBN: 978-3-319-22053-6
eBook Packages: Computer ScienceComputer Science (R0)