Abstract

Electrocardiogram (ECG) signals are vital to identifying cardiovascular disease. The numerous availability of signal processing and neural networks techniques for processing of ECG signals has inspired us to do research on extracting features of ECG signals to identify different cardiovascular diseases. We distinguish between a healthy person ECG data and person having disease ECG data using signal processing and neural network toolbox in Matlab. The data was downloaded from physiobank. To distinguish normal and abnormal ECG, Neural network is used. Feature extraction method is used to identify heart diseases. The diseases that are identified include Tachycardia, Bradycardia, first- degree Atrioventricular (AV) and a healthy person. Subsequently, ECG signals are very noisy; signal processing techniques are used to remove the noise impurity. The heart rate can be calculated by detecting the distance between R-R intervals of the signal. The algorithm successfully distinguished between normal and abnormal ECG data.

Keywords: Electrocardiogram (ECG), Cardiovascular Disease, Bradycardia, Tachycardia, atrioventricular, Neural Network, MATLAB, Physiobank

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How to Cite
[1]
Shah, K. et al. 2018. Detection of Heart Defects using Electrocardiogram (ECG). International Journal of Science and Engineering Invention. 4, 11 (Nov. 2018), 15 to 19. DOI:https://doi.org/10.23958/ijsei/vol04-i11/120.

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Copyright © 2018 Kenil Shah Mayur Rane Dr. Vahid Emamian this is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.