By Topic

Analysis of the sustained ventricular arrhythmias from SAECG using artificial neural network and fuzzy clustering algorithm

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

4 Author(s)
Heidari, H. ; Dept. of Electr. Eng., Sahand Univ. of Technol., Tabiz, Iran ; Shahidi, A.V. ; Aminian, K. ; Sadati, N.

Patients with sustained ventricular tachycardia and ventricular fibrillation have a potential for sudden death. After myocardial infarction the chance to get sustained ventricular tachycardia or ventricular fibrillation increases, thus reduction in number of sudden death requires advanced predictive procedures. In this study, frequency domain feature extraction, clustering and classification models are combined for providing an integrated system for the sustained ventricular arrythmias. The radial basis function network and the fuzzy c-means algorithm for training clustering and classification were investigated. These techniques do not have limitation of the previous classical procedures. The value of sensitivity and specificity, on the data was used here, for the RBFN were found to be 92.3% and 71.4%, respectively, also the value of sensitivity and specificity for the PCM were found to be 84.6% and 71.4%, respectively

Published in:

Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE

Date of Conference:

29 Oct-1 Nov 1998