Classification of Myocardial Infarction using Convolution Neural Network
K.Venu1, P.Natesan2, B.Krishnakumar3, N.Sasipriya4
1K.Venu*, Department of CSE, Kongu Engineering College, Erode, India.
2Dr.P.Natesan, Department of CSE, Kongu Engineering College, Erode, India.
3B.Krishnakumar, Department of CSE,Kongu Engineering College,Erode, India.
4N.Sasipriya, Department of CSE, Kongu Engineering College,Erode, India.
Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 12763-12768 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9230118419/2019©BEIESP | DOI: 10.35940/ijrte.D9230.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Myocardial infarction is one of the most dangerous cardiovascular diseases for most of the people in the world. It is generally confessed as a heart attack. The damage of the heart muscle causes the Myocardial Infraction (MI). When there is a block in heart veins, then the flow of oxygen to the heart muscle also gets blocked, which leads to damage of the heart muscle. The damage is irreversible, so it may lead to death. Quick and exact recognition of MI is required to reduce the death rate. There are several diagnostic tools such as blood tests, ECG is available for the analysis of cardiovascular disease. Among all tools, ECG provides effective results in determining MI, but the manual interpretation of the ECG signal may take time for the doctor to identify the symptoms of MI. The manual interpretation may vary from person to person. Hence a computer-aided diagnostic tool is required to analyze ECG signals effectively for identifying MI. This paper aims to provide an algorithm for the detection of myocardial infarction that operates directly on ECG data. Nowadays Convolutional neural network is cable of analyzing an image effectively so, a deep learning model with the CNN algorithm is used in this paper to classify the images and to identify whether the image has MI or not. The proposed CNN model yields 87% accuracy for the Physikalisch-Technische Bundesanstalt database.
Keywords: Convolution Neural Network, CNN, Deep Learning, Myocardial Infraction, Classification
Scope of the Article: Deep Learning.