Paper Info
Title
Detection of myocardial scar from the VCG using a supervised learning approach.
Abstract
This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the investigation of vectorcardiogram (VCG) characteristics. Scarred myocardium is the result of myocardial infarction (MI) due to ischemia and creates a substrate for the manifestation of fatal arrhythmias. Our efforts are focused on the development of a classification scheme for the early screening of patients for the presence of scar. More specifically, a supervised learning model based on the extracted VCG features is proposed and validated through comprehensive testing analysis. The achieved accuracy of 82.36% (sensitivity 84.31%, specificity 77.36%) indicates the potential of the proposed screening mechanism for detecting the presence/absence of scar tissue.
Year
DOI
Venue
2013
10.1109/EMBC.2013.6611250
EMBC
Keywords
Field
DocType
supervised learning approach,cardiology,diseases,ischemia,vectorcardiogram characteristics,vcg,myocardial infarction,learning (artificial intelligence),medical signal processing,early patient screening,fatal arrhythmia,scar tissue,myocardial scar,myocardium,feature extraction,bioelectric phenomena,signal classification,myocardial scar detection,biological tissues,vcg feature extraction,patient diagnosis,vectors,svm classification,databases,support vector machines,heart,learning artificial intelligence
Myocardial infarction,Computer vision,Internal medicine,Cardiology,Classification scheme,Ischemia,Supervised learning,Signal classification,Artificial intelligence,Medicine,Pathology
Conference
Volume
ISSN
Citations 
2013
1557-170X
1
PageRank 
References 
Authors
0.36
2
7
Name
Order
Citations
PageRank
Christos Panagiotou1243.54
Sofia-Maria Dima292.24
Evangelos B Mazomenos310.36
James A Rosengarten4513.80
Koushik Maharatna5114.05
John V. Gialelis64310.30
John M. Morgan7605.81