Cardiac Arrhythmia Detection in an ECG Beat Signal Using 1D Convolution Neural Network

The change in the normal rhythm of a human heart can lead to different cardiac arrhythmia. To monitor the functionality of the cardiovascular system by electrocardiogram (ECG) is used reliably. Recently, the accurate categorization of the heartbeats is accepted to be a very concerned incident. In this paper, we applied convolution neural network (CNN) to classify five classes of human heartbeat category and had used the generated classified output for information fusion towards a smart heart monitoring system (SHMS) that consist a smart-phone application with few patient-friendly features for individuals. We evaluated the system on Physio Net's several datasets. According to the findings, the system can classify five different types of heart rhythm with an average accuracy of 98:28%. This project aims to maintain optimum growing conditions of the human electrocardiogram frequently.