A Neural Network based Cardiac Arrhythmia Diagnosis system from Dynamic Features of Electrocardiogram Signal

Objectives: Cardiac arrhythmia is a type of disorder where the heartbeat is irregular, too slow, or too fast. As a result of heart diseases, there is an increase in death yearly. The early detection of cardiac diseases is important for preventing the deaths due to the cardiac diseases. Methods: The Electrocardiogram (ECG) is used to record the electrical activity of the heart for physician to diagnose the heart diseases. In this study, we propose a cardiac diagnosis system for diagnosing cardiac arrhythmia disease. It will be most helpful for the patients who undergone a heart surgery for continuous monitoring of post-surgical status. Findings: The major objective of this paper is to implement an effective algorithm to discriminate between the normal and diseased persons. The Monitoring process includes the following tasks, such as preprocessing and feature extraction by Pan Tompkin’s algorithm and the features are classified using neural network and support vector machine. The performance of the classifiers was evaluated using the parameters such as sensitivity, specificity and accuracy. The Accuracy of the neural network algorithm is 88.54% and the accuracy of the Support Vector Machine is 84.37%. Application/Improvements: The Neural network classifier shows better performance compared to support vector machine. In future the classifier is trained using the best set of features using feature selection techniques. *