ECG Classification For Arrhythmias using CNN & Heart Disease Prediction using Web application

Abstract

The prevalence and mortality rates of cardiovascular disease (CVD) continue to rise. As a result, frequent cardiac rhythm monitoring has become an increasingly critical and vital aspect of managing and preventing CVDs. The automatic diagnosis of cardiac illness relies heavily on the classification of electrocardiogram signals. A stroke can result in brain damage and necessitates immediate medical attention. To diagnose an arrhythmia, a doctor must first recognize the abnormal heartbeat and attempt to determine its cause or trigger. Thanks to the development of artificial intelligence and Science that has enabled us to predict the cases of arrhythmia far better than doctors by the use of Convolutional Neural Networks. We in this project aim to diagnose the type of arrhythmias by the spectrograms of numerical data of the ECG images.