By Topic

Artificial Neural Network based cardiac arrhythmia classification using ECG signal data

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
$33 $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

3 Author(s)
S. M. Jadhav ; Department of Information Technology, Dr. Babasaheb Ambedkar Technological University, Lonere-402103 Dist. Raigad (M.S.), India ; S. L. Nalbalwar ; Ashok Ghatol

In this paper we proposed a automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia using standard 12 lead ECG recordings. In this study, we are mainly interested in producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. In arrhythmia analysis, it is unavoidable that some attribute values of a person would be missing. Therefore we have replaced these missing attributes by closest column value of the concern class. Multilayer percepron (MLP) feedforward neural network model with static backpropagation algorithm is used to classify arrhythmia cases into normal and abnormal classes. Networks models are trained and tested for UCI ECG arrhythmia data set. This data set is a good environment to test classifiers as it is incomplete and ambiguous bio-signal data collected from total 452 patient cases. The classification performance is evaluated using six measures; sensitivity, specificity, classification accuracy, mean squared error (MSE), receiver operating characteristics (ROC) and area under curve (AUC). Our experimental results give 86.67% testing classification accuracy.

Published in:

Electronics and Information Engineering (ICEIE), 2010 International Conference On  (Volume:1 )

Date of Conference:

1-3 Aug. 2010