By Topic

Automated heart abnormality detection using sparse linear classifiers

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Qazi, M. ; Siemens Med. Solutions, Malvern, PA ; Fung, G. ; Krishnan, S. ; Jinbo Bi
more authors

In this article, the task of building a computer-aided diagnosis system that can automatically detect wall-motion abnormalities from echocardiograms was addressed. Some medical background on cardiac ultrasound and the standard methodology used by cardiologists to score wall-motion abnormalities were provided. Real-life dataset, which consists of echocardiograms used by cardiologists at St. Francis Heart Hospital to diagnose wall-motion abnormalities were also described. The paper provides an overview of the proposed system, which was built on top of an algorithm that detects and tracks the inner and outer walls of the heart. It consists of a classifier that classifies the local region of the heart wall (and the entire heart) as normal or abnormal based on the wall motion. A methodology for feature selection and classification, followed by our experimental results was also described. The novel feature selection technique results in a robust hyperplane-based classifier that achieves the best performance in terms of AUC (area under the curve) and number of features selected when compared to three other well-known classification algorithms

Published in:

Engineering in Medicine and Biology Magazine, IEEE  (Volume:26 ,  Issue: 2 )