TY - GEN

T1 - A Multiple Kernel Learning Framework to Investigate the Relationship Between Ventricular Fibrillation and First Myocardial Infarction

AU - Marciniak, Maciej

AU - Arevalo, Hermenegild

AU - Tfelt-Hansen, Jacob

AU - Ahtarovski, Kiril A.

AU - Jespersen, Thomas

AU - Jabbari, Reza

AU - Glinge, Charlotte

AU - Vejlstrup, Niels

AU - Engstrom, Thomas

AU - Maleckar, Mary M.

AU - McLeod, Kristin

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-59448-4_16

DO - 10.1007/978-3-319-59448-4_16

M3 - Article in proceedings

AN - SCOPUS:85020473723

SN - 9783319594477

T3 - Lecture Notes in Computer Science

SP - 161

EP - 171

BT - Functional Imaging and Modelling of the Heart

PB - Springer

T2 - 9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017

Y2 - 11 June 2017 through 13 June 2017

ER -