Neural Network Approaches for Real-Time Detection of Cardiovascular Abnormalities

Abstract

The early detection of cardiovascular diseases could be life-saving, especially when the location of the patient is considered. Therefore, in recent years, work has been done on the early detection of cardiovascular diseases. The common point of deep learning models developed for the detection of cardiovascular diseases is the use of complex models. The complex model not only increases the amount of calculations but also prevents real-time use for the detection of cardiac diseases. In this study, by using simple deep learning models, the aim is to determine the deep learning model that allows the real-time detection of cardiovascular diseases. For this purpose, in the study, the models developed using convolutional neural networks and time-frequency information obtained with discrete wavelet transform were analyzed.