Abstract
Myocardial infarction results in changes in the structure and tissue deformation of the ventricles. In some cases, the development of the disease may trigger an arrhythmic event, which is a major cause of death within the first twenty four hours after the infarction. Advanced analysis methods are increasingly used in order to discover particular characteristics of the myocardial infarction development that lead to the occurrence of arrhythmias. However, such methods usually consider only a single feature or combine separate analyses from multiple features in the analytical process. In an attempt to address this, we propose to use cardiac magnetic resonance imaging to extract data on the shape of the ventricles and volume and location of the infarct zone, and to combine them within one analytical model through a multiple kernel learning framework. The proposed method was applied to a cohort of 46 myocardial infarction patients. The location, rather than the volume, of the infarct region was found to be correlated with arrhythmic events and the proposed combination of kernels yielded excellent accuracy (100%) in distinguishing between patients that did and did not present at the hospital with ventricular fibrillation.
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Pfeffer, M.A., Braunwald, E.: Ventricular remodeling after myocardial infarction. experimental observations and clinical implications. Circulation 81(4), 1161–1172 (1990)
Gorenek, B., Lundqvist, C.B., Terradellas, J.B., Camm, A.J., Hindricks, G., Huber, K., Kirchhof, P., Kuck, K.H., Kudaiberdieva, G., Lin, T., et al.: Cardiac arrhythmias in acute coronary syndromes: position paper from the joint ehra, acca, and eapci task force. Europace 16, 1655–1673 (2014). euu208
Peressutti, D., Sinclair, M., Bai, W., Jackson, T., Ruijsink, J., Nordsletten, D., Asner, L., Hadjicharalambous, M., Rinaldi, C.A., Rueckert, D., et al.: A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: application to cardiac resynchronisation therapy response prediction. Med. Image Anal. 35, 669–684 (2017)
Ismail, T.F., Prasad, S.K., Pennell, D.J.: Prognostic importance of late gadolinium enhancement cardiovascular magnetic resonance in cardiomyopathy. Heart 98(6), 438–442 (2012)
Durrleman, S., Pennec, X., Trouvé, A., Ayache, N.: Statistical models of sets of curves and surfaces based on currents. Med. Image Anal. 13(5), 793–808 (2009)
Vapnik, V.: The Nature of Statistical Learning Theory. Wiley, New York (1998)
Gönen, M., Alpaydın, E.: Multiple kernel learning algorithms. J. Mach. Learn. Res. 12, 2211–2268 (2011)
Jabbari, R., Engstrøm, T., Glinge, C., Risgaard, B., Jabbari, J., Winkel, B.G., Terkelsen, C.J., Tilsted, H.H., Jensen, L.O., Hougaard, M., et al.: Incidence and risk factors of ventricular fibrillation before primary angioplasty in patients with first st-elevation myocardial infarction: a nationwide study in Denmark. J. Am. Heart Assoc. 4(1), e001399 (2015)
Heiberg, E., Sjögren, J., Ugander, M., Carlsson, M., Engblom, H., Arheden, H.: Design and validation of segment-freely available software for cardiovascular image analysis. BMC Med. Imaging 10(1), 1 (2010)
Marciniak, M., et al.: From CMR image to patient-specific simulation and population-based analysis: tutorial for an openly available image-processing pipeline. In: Mansi, T., McLeod, K., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2016. LNCS, vol. 10124, pp. 106–117. Springer, Cham (2017). doi:10.1007/978-3-319-52718-5_12
Hergan, K., Schuster, A., Frühwald, J., Mair, M., Burger, R., Töpker, M.: Comparison of left and right ventricular volume measurement using the Simpson’s method and the area length method. Eur. J. Radiol. 65(2), 270–278 (2008)
Lanckriet, G.R., Cristianini, N., Bartlett, P., Ghaoui, L.E., Jordan, M.I.: Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res. 5, 27–72 (2004)
He, J., Chang, S.F., Xie, L.: Fast kernel learning for spatial pyramid matching. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–7. IEEE (2008)
Qiu, S., Lane, T.: A framework for multiple kernel support vector regression and its applications to sirna efficacy prediction. IEEE/ACM Trans. Comput. Biol. Bioinf. 6(2), 190–199 (2009)
Cristianini, N., Elisseeff, A., Shawe-Taylor, J., Kandola, J.: On kernel-target alignment. In: Advances in Neural Information Processing Systems (2001)
Acknowledgements
This project was partially carried out in the Centre for Cardiological Innovation (CCI), Norway funded by the Norwegian Research Council, and partially funded by the Novo Nordisk foundation.
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Marciniak, M. et al. (2017). A Multiple Kernel Learning Framework to Investigate the Relationship Between Ventricular Fibrillation and First Myocardial Infarction. In: Pop, M., Wright, G. (eds) Functional Imaging and Modelling of the Heart. FIMH 2017. Lecture Notes in Computer Science(), vol 10263. Springer, Cham. https://doi.org/10.1007/978-3-319-59448-4_16
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DOI: https://doi.org/10.1007/978-3-319-59448-4_16
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