학술논문

Bayesian classification applied to strain in arrythmogenic left-ventricle cardiomyopathy
Document Type
Conference
Source
2017 Computing in Cardiology (CinC) Computing in Cardiology (CinC), 2017. :1-4 Sep, 2017
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Strain
Myocardium
Imaging
Machine learning algorithms
Diseases
Frequency selective surfaces
Magnetic resonance
Language
ISSN
2325-887X
Abstract
Arrhythmogenic cardiomyopathy (AC) is a rare disease associated with ventricular arrhythmias and sudden cardiac death. While AC of the right ventricle has been more extensively studied, exclusive left-ventricle involvement needs to be better characterized. Myocardial strain, obtained by feature tracking, provide insight into its biomechanical behavior. To characterize it, multivariate classifiers can be applied. The sample consisted of 13 AC-LV and 13 non-carriers of the mutation. The feature tracking algorithm of Circle cvi 42 was applied to the cardiac magnetic resonance of each patient. A Naïve Bayes classifier with a feature subset selection method was applied to the parameters of peak strain, strain rate, displacement and velocity. We obtained an accuracy of 90% in NB and we arrived to 93% for CFS-NB. The strain parameters selected by the FSS algorithm were three: longitudinal peak strain and peak systolic and diastolic velocities. In all the selected features, AC-LV patients had smaller values as controls. In conclusion, myocardial strain is affected in AC-LV patients. Naïve Bayes classifiers allow obtaining a good discriminating accuracy among groups.