On Modeling the Cardiovascular System and Predicting the Human Heart Rate under Strain

Melanie Ludwig, Ashok Meenakshi Sundaram, Matthias Füller, Alexander Asteroth, Erwin Prassler

2015

Abstract

With the increasing average age of the population in many developed countries, afflictions like cardiovascular diseases have also increased. Exercising has a proven therapeutic effect on the cardiovascular system and can counteract this development. To avoid overstrain, determining an optimal training dose is crucial. In previous research, heart rate has been shown to be a good measure for cardiovascular behavior. Hence, prediction of the heart rate from work load information is an essential part in models used for training control. Most heart-rate-based models are described in the context of specific scenarios, and have been evaluated on unique datasets only. In this paper, we conduct a joint evaluation of existing approaches to model the cardiovascular system under a certain strain, and compare their predictive performance. For this purpose, we investigated some analytical models as well as some machine learning approaches in two scenarios: prediction over a certain time horizon into the future, and estimation of the relation between work load and heart rate over a whole training session.

References

  1. Baig, D.-e.-Z., Su, H., Cheng, T., Savkin, A., Su, S., and Celler, B. (2010). Modeling of human heart rate response during walking, cycling and rowing. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 2553-2556.
  2. Brzostowski, K., Drapala, J., Grzech, A., and Swiatek, P. (2013). Adaptive decision support system for automatic physical effort plan generation - data-driven approach. Cybernetics and Systems, 44(2-3):204-221.
  3. Busso, T., Denis, C., Bonnefoy, R., Geyssant, A., and Lacour, J.-R. (1997). Modeling of adaptations to physical training by using a recursive least squares algorithm. Journal of applied physiology, 82(5):1685- 1693.
  4. Calvert, T. W., Banister, E. W., Savage, M. V., and Bach, T. (1976). A systems model of the effects of training on physical performance. IEEE Transactions on Systems, Man and Cybernetics, (2):94-102.
  5. Cheng, T., Savkin, A., and Celler, B. (2008). Nonlinear modeling and control of human heart rate response during exercise with various work load intensities. Biomedical Engineering, IEEE Transactions on.
  6. Cheng, T. M., Savkin, A. V., Celler, B. G., Wang, L., and Su, S. W. (2007). A nonlinear dynamic model for heart rate response to treadmill walking exercise. In 2007 IEEE Int. Conf. on Engineering in Medicine and Biology Society (EMBS), pages 2988-2991. IEEE.
  7. Costa, T., Boccignone, G., and Ferraro, M. (2012). Gaussian mixture model of heart rate variability. PloS one, 7(5):e37731.
  8. Feng Xiao, Yimin Chen, Ming Yuchi, Mingyue Ding, and Jun Jo (2010). Heart Rate Prediction Model Based on Physical Activities Using Evolutionary Neural Network. In 2010 Fourth International Conference on Genetic and Evolutionary Computing, pages 198- 201. IEEE.
  9. Graf, C., Bjarnason-Wehrens, B., Rost, R., Foitschik, T., Lagerström, D., and Quilling, E. (2014). Sportund Bewegungstherapie bei inneren Krankheiten: Lehrbuch für Sportlehrer, Ü bungsleiter, Physiotherapeuten und Sportmediziner. Deutscher Ïrzte-Verlag.
  10. Hajek, M., Potucek, J., and Brodan, V. (1980). Mathematical model of heart rate regulation during exercise. Automatica, 16(2):191-195.
  11. Javed, F., Chan, G. S. H., Savkin, A. V., Middleton, P. M., Malouf, P., Steel, E., Mackie, J., and Lovell, N. H. (2009). RBF kernel based support vector regression to estimate the blood volume and heart rate responses during hemodialysis. International Conference of the IEEE Engineering in Medicine and Biology Society, 2009:4352-5.
  12. Koenig, A., Somaini, L., and Pulfer, M. (2009). Modelbased heart rate prediction during lokomat walking. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE.
  13. Lefever, J., Berckmans, D., and Aerts, J.-M. (2014). Timevariant modelling of heart rate responses to exercise intensity during road cycling. European Journal of Sport Science, 14(sup1):S406-S412.
  14. Leitner, T., Kirchsteiger, H., Trogmann, H., and del Re, L. (2014). Model based control of human heart rate on a bicycle ergometer. In Control Conference (ECC), 2014 European, pages 1516-1521. IEEE.
  15. Mohammad, S., Guerra, T. M., GROBOIS, J. M., and Hecquet, B. (2011). Heart rate control during cycling exercise using takagi-sugeno models. In 18th IFAC World Congress, Milano (Italy).
  16. Müller, F., Mülller, S., Helmer, A., and Hein, A. (2014). Evaluation of a generic heart rate model for exercise planning and execution across training modalities.
  17. Nichols, M., Townsend, N., Luengo-Fernandez, R., Leal, J., Gray, A., Scarborough, P., and Rayner, M. (2012). European Cardiovascular Disease Statistics 2012. European Heart Network, Brussels, European Society of Cardiology, Sophia Antipolis.
  18. Paradiso, M., Pietrosanti, S., Scalzi, S., Tomei, P., and Verrelli, C. (2013). Experimental heart rate regulation in cycle-ergometer exercises. IEEE Transactions on Biomedical Engineering, 60(1):135-139.
  19. Rosenblatt, F. (1961). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms.
  20. Seal, H. L. (1967). Studies in the History of Probability and Statistics. XV The historical development of the Gauss linear model. Biometrika, 54(1-2):1-24.
  21. Smola, A. J. and Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3):199-222.
  22. Su, S., Wang, L., Celler, B., Savkin, A., and Guo, Y. (2007). Identification and control for heart rate regulation during treadmill exercise. Biomedical Engineering, IEEE Transactions on, 54(7):1238-1246.
  23. Sumida, M., Mizumoto, T., and Yasumoto, K. (2013). Estimating heart rate variation during walking with smartphone. page 245. ACM Press.
  24. Tabachnick, B. G. and Fidell, L. S. (2006). Using Multivariate Statistics (5th Edition).
  25. Vapnik, V. (1995). The Nature of Statistical Learning Theory.
  26. Velikic, G., Modayil, J., Thomsen, M., Bocko, M., and Pentland, A. (2011). Predicting the near-future impact of daily activities on heart rate for at-risk populations. In e-Health Networking Applications and Services (Healthcom), 2011 13th IEEE International Conference on, pages 94-97. IEEE.
  27. Wang, L., Su, S. W., and Celler, B. G. (2009). Assessing the human cardiovascular response to moderate exercise: feature extraction by support vector regression. Physiological Measurement.
  28. WHO (2012). Demographic change, life expectancy and mortality trends in europe: fact sheet. In The European health report 2012. World Health Organization.
  29. Zhang, Y. (2013). Monitoring, Modeling, and Regulation for Indoor and Outdoor Exercises. PhD thesis, University of Technology, Sydney.
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Paper Citation


in Harvard Style

Ludwig M., Meenakshi Sundaram A., Füller M., Asteroth A. and Prassler E. (2015). On Modeling the Cardiovascular System and Predicting the Human Heart Rate under Strain . In Proceedings of the 1st International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AgeingWell, ISBN 978-989-758-102-1, pages 106-117. DOI: 10.5220/0005449001060117


in Bibtex Style

@conference{ict4ageingwell15,
author={Melanie Ludwig and Ashok Meenakshi Sundaram and Matthias Füller and Alexander Asteroth and Erwin Prassler},
title={On Modeling the Cardiovascular System and Predicting the Human Heart Rate under Strain},
booktitle={Proceedings of the 1st International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AgeingWell,},
year={2015},
pages={106-117},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005449001060117},
isbn={978-989-758-102-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AgeingWell,
TI - On Modeling the Cardiovascular System and Predicting the Human Heart Rate under Strain
SN - 978-989-758-102-1
AU - Ludwig M.
AU - Meenakshi Sundaram A.
AU - Füller M.
AU - Asteroth A.
AU - Prassler E.
PY - 2015
SP - 106
EP - 117
DO - 10.5220/0005449001060117