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
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
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.
UR - http://www.scopus.com/inward/record.url?scp=85020473723&partnerID=8YFLogxK
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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 161
EP - 171
BT - Functional Imaging and Modelling of the Heart - 9th International Conference, FIMH 2017, Proceedings
A2 - Pop, Mihaela
A2 - Wright, Graham A.
PB - Springer Verlag
T2 - 9th International Conference on Functional Imaging and Modelling of the Heart, FIMH 2017
Y2 - 11 June 2017 through 13 June 2017
ER -