Prediction of Cardiac Arrhythmia using Artificial Neural Network
V. Sai Krishna1, A. Nithya Kalyani2

1V. Sai Krishna, B.Tech Scholar, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kanchipuram (T.N), India.
2A. Nithya Kalyani, Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kanchipuram (T.N), India.
Manuscript received on 04 June 2019 | Revised Manuscript received on 29 June 2019 | Manuscript Published on 04 July 2019 | PP: 462-466 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A10860681S419/2019©BEIESP
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Abstract: Cardiac Arrhythmia is a condition in which the heartbeat is abnormal. Different medical data mining and machine learning techniques are being implemented to extract valuable information regarding heart disease prediction. But, the accuracy of the desired results is not yet satisfactory. The lack of specialist doctors and increase in wrong diagnosed cases has necessitated the need for building an efficient heart disease detection system. The aim of this paper is to classify the ECG signal data of a person into different types of Arrhythmia such as Bradycardia, Tachycardia etc. After appropriate feature selection, the plan is to solve this problem by developing a heart attack prediction system using Deep learning techniques, such as Recurrent Neural Networks to predict the likely possibilities of Cardiac Arrhythmia in a patient. This paper presents a survey of recent techniques for prediction of Cardiac Arrhythmia and their methodologies.
Keywords: Cardiac Arrhythmia, ECG, Deep Learning, Recurrent Neural Network.
Scope of the Article: Regression and Prediction