Abstract
The ejection fraction (EF) is a parameter that represents the amount of blood pumped out of each ventricle in each cardiac cycle and can be used for analyzing the heart failure. There are several diagnostic tests to determine whether the person has heart failure but some are expensive tests and they do not allow obtaining continuous estimations of EF. However, use the Electrical Impedance Tomography (EIT) with Regression Models is an alternative to obtain continuous estimations of EF. The quality of EIT is that it allows a quick diagnosis about the heart’s health, combining low cost and high portability. This paper it proposed four regression models, using the electrical measures from EIT, to estimate the EF : Gaussian Processes (GP), Support Vector Regression (SVR), Elastic Net Regression (ENR) and Multivariate Adaptive Regression Splines (MARS). The overall evaluation of results show that all models achieved competitive results and the method SVR has produced better results than the others tested.
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
Py-earth documentation. http://contrib.scikit-learn.org/py-earth/. Accessed 01 Aug 2016
Bekeredjian, R., Grayburn, P.A.: Valvular heart disease: aortic regurgitation. Circulation 112(9), 125–134 (2005)
Blanc, C., Schlick, C.: X-splines: a spline model designed for the end-user. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques. SIGGRAPH 1995, pp. 377–386. ACM, New York (1995)
Djolonga, J., Krause, A., Cevher, V.: High-dimensional gaussian process bandits. In: Advances in Neural Information Processing Systems, pp. 1025–1033 (2013)
Filho, R.G.N.S., Campos, L.C.D., dos Santos, R.W., Barra, L.P.S.: Determination of cardiac ejection fraction by electrical impedance tomography using an artificial neural network. In: Castro, F., Gelbukh, A., González, M. (eds.) MICAI 2013, Part II. LNCS, vol. 8266, pp. 130–138. Springer, Heidelberg (2013)
Friedman, J.H.: Multivariate adaptive regression splines. Ann. Stat. 19, 1–67 (1991)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning - Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2009)
Hozumi, T., Shakudo, M., Shah, P.: Quantitation of left ventricular volumes and ejection fraction by biplane transesophageal echocardiography. Am. J. Cardiol. 72, 356–359 (1993). Elsevier Inc
Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14, 1137–1145 (1995)
MacIver, D.H., Adeniran, I., Zhang, H.: Left ventricular ejection fraction is determined by both global myocardial strain and wall thickness. IJC Heart Vasculature 7, 113–118 (2015)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in python. J Mach. Learn. Res. 12, 2825–2830 (2011)
Peters, F.C., Barra, L.P.d.S., dos Santos, R.W.: Determination of cardiac ejection fraction by electrical impedance tomography. INTECH Open Access Publisher (2011)
Peters, F.C., Barra, L.P.S., dos Santos, R.W.: Determination of cardiac ejection fraction by electrical impedance tomography - numerical experiments and viability analysis. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009, Part I. LNCS, vol. 5544, pp. 819–828. Springer, Heidelberg (2009)
Regeer, M.V., Versteegh, M.I., Klautz, R.J., Schalij, M.J., Bax, J.J., Marsan, N.A., Delgado, V.: Comparison of left ventricular volume and ejection fraction and frequency and extent of aortic regurgitation after operative repair of type a aortic dissection among three different surgical techniques. Am. J. Cardiol. 117(7), 1167–1172 (2016)
Ribeiro, M.H., dos Santos, R.W., Barra, L.P.S., Peters, F.C.: Simulation study on the determination of cardiac ejection fraction by electrical impedance tomography using a hybrid heuristic approach. J. Med. Imag. Health Inf. 4(1), 113–121 (2014)
Sacks, J., Welch, W.J., Mitchell, T.J., Wynn, H.P.: Design and analysis of computer experiments. Stat. Sci. 4, 409–423 (1989)
Filho, R.G.N.S., Campos, L.C.D., dos Santos, R.W., Barra, L.P.S.: Artificial neural networks ensemble applied to the electrical impedance tomography problem to determine the cardiac ejection fraction. In: Bazzan, A.L.C., Pichara, K. (eds.) IBERAMIA 2014. LNCS, vol. 8864, pp. 734–741. Springer, Heidelberg (2014)
da Silva Barra, L.P., Peters, F.C., de Paula Martins, C., Barbosa, H.J.C.: Computational experiments in electrical impedance tomography. In: XXVII Iberian Latin American Congress on Computational Methods in Engineering (2006)
da Silva Barra, L.P., dos Santos, R.W., Peters, F.C., Santos, E.P., Barbosa, H.J.C.: Parallel computational experiments in electrical impedance tomography. 18th Symp. Comput. Archit. High Perform. Comput. 1, 7–13 (2006)
Squeri, A., Gaibazzi, N., Reverberi, C., Caracciolo, M.M., Ardissino, D., Gherli, T.: Ejection fraction change and coronary artery disease severity: a vasodilator contrast stress-echocardiography study. J. Am. Soc. Echocardiogr. 25(4), 454–459 (2012)
Upadhya, B., Taffet, G.E., Cheng, C.P., Kitzman, D.W.: Heart failure with preserved ejection fraction in the elderly: scope of the problem. J. Mol. Cell. Cardiol. 83, 73–87 (2015). Perspectives on Cardiovascular Aging: Top to Bottom
Were, K., Bui, D.T., Øystein, B.D., Singh, B.R.: A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an afromontane landscape. Ecol. Ind. 52, 394–403 (2015)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc.: Ser. B (Stat. Methodol.) 67(2), 301–320 (2005)
Acknowledgments
The authors would like to thank the anonymous referees for their comments, which helped us to improve a previous version of this paper. The authors would like to thank the Brazilian agencies FAPEMIG (grant 01606/15), CNPq and CAPES for financial support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Fonseca, T.L., Goliatt, L., Campos, L.C.D., Bastos, F.S., Barra, L.P.S., dos Santos, R.W. (2016). Machine Learning Approaches to Estimate Simulated Cardiac Ejection Fraction from Electrical Impedance Tomography. In: Montes y Gómez, M., Escalante, H., Segura, A., Murillo, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2016. IBERAMIA 2016. Lecture Notes in Computer Science(), vol 10022. Springer, Cham. https://doi.org/10.1007/978-3-319-47955-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-47955-2_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47954-5
Online ISBN: 978-3-319-47955-2
eBook Packages: Computer ScienceComputer Science (R0)