CNN based deep learning methods for precise analysis of cardiac arrhythmias

Abstract

In contemporary day, Deep Learning (DL) is a developing discipline in the science of Machine Learning (ML) (ML). The research in this field is evolving extremely fast and its consequence leads to breakthrough in advance technology. Deep learning approaches are meant to gradually learn characteristics from several layers by adopting a general purpose learning mechanism, without relying on the human built features. This enables the system to learn the complicated functions and translate the input to the output straight from the data. This study effort primarily focuses on emphasising the Convolutional Neural Networks (CNNs), a kind of Deep Neural Networks (DNNs) and develop an 11 layered CNN for effective ECG arrhythmia classification. In this study the relevance of CNNs, the major building blocks and layers are explored, the design of the suggested CNN model is described.