Journal of Biosciences and Medicines, 2017, 5, 64-79 http://www.scirp.org/journal/jbm ISSN Online: 2327-509X ISSN Print: 2327-5081 DOI: 10.4236/jbm.2017.511008 Nov. 27, 2017 64 Journal of Biosciences and Medicines Classification of Cardiovascular Disease Using Feature Extraction and Artificial Neural Networks Shalin Savalia * , Eder Acosta, Vahid Emamian Department of Electrical Engineering, St. Mary’s University, San Antonio, USA Abstract Electrocardiogram (ECG) signals are used to identify cardiovascular disease. The availability of signal processing and neural networks techniques for processing ECG signals has inspired us to do research that consists of extract- ing features of an ECG signals to identify types of cardiovascular diseases. We distinguish between normal and abnormal ECG data using signal processing and neural networks toolboxes in Matlab. Data, which are downloaded from an ECG database, Physiobank, are used for training and testing the neural network. To distinguish normal and abnormal ECG with the significant accu- racy, pattern recognition tools with NN is used. Feature Extraction method is also used to identify specific heart diseases. The diseases that were identified include Tachycardia, Bradycardia, first-degree Atrioventricular (AV), and second-degree Atrioventricular. Since ECG signals are very noisy, signal processing techniques are applied to remove the noise contamination. The heart rate of each signal is calculated by finding the distance between R-R in- tervals of the signal. The QRS complex is also used to detect Atrioventricular blocks. The algorithm successfully distinguished between normal and abnor- mal data as well as identifying the type of disease. Keywords Electrocardiogram (ECG), Cardiovascular Disease, MATLAB, Artificial Neural Network, Physiobank, R-R interval, Matlab, QRS Complex, Atrioventricular, Tachycardia, Bradycardia 1. Introduction An electrocardiogram (ECG) is a measure of how the electrical activity of the heart varies with respect to time as action potentials propagate throughout the How to cite this paper: Savalia, S., Acosta, E. and Emamian, V. (2017) Classification of Cardiovascular Disease Using Feature Ex- traction and Artificial Neural Networks. Journal of Biosciences and Medicines, 5, 64-79. https://doi.org/10.4236/jbm.2017.511008 Received: October 19, 2017 Accepted: November 24, 2017 Published: November 27, 2017 Copyright © 2017 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access