International Symposium on Telecommunication (IST)

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Classification of Congestive Heart Failure Disease, Arrhythmias, And Normal Heart Rhythms Using Deep Learning

Authors :

Nafiseh Batavani¹ Mohammad Reza Yousefi²

1- Najafabad Branch, Islamic Azad University 2- Najafabad Branch, Islamic Azad University

Keywords :

Deep learning،Convolutional neural network،Congestive heart failure،Cardiac arrhythmia،ECG signal

Abstract :

Cardiovascular diseases, Regardless of the reason for their occurrence., are one of the leading causes of death worldwide. For this reason, the issue of time is very important in diagnosing cardiovascular diseases. If a heart disease is diagnosed in a patient by a doctor in a timely manner and then the prescribed solution for treating the disease is properly implemented, not only will it prevent the end of life, but it will also greatly improve the quality of life for individuals suffering from heart diseases. Given the limitations and various challenges in diagnosing cardiac diseases from normal sinus rhythms, the presentation of automated machine learning methods is essential and highly important. The aim of this article is to present an efficient method for classifying congestive heart failure disease from other arrhythmias and normal heart rhythms using a combined network of convolutional neural networks and gated recurrent unit networks (CNN+GRU). To this end, in the first stage, a dataset consisting of electrocardiogram (ECG) signals in three groups of normal sinus rhythm, congestive heart failure, and cardiac arrhythmias was extracted from the PhysioNet database, and preprocessing and noise removal of the signals were performed. In the second stage, continuous wavelet transform has been used to convert ECG signals into two-dimensional matrices. In the third stage, classification was performed using a combined model of convolutional neural networks and gated recurrent unit networks. Finally, the results obtained from two methods, Convolutional Neural Networks (CNN) and a combined approach of Convolutional Neural Networks and Gated Recurrent Unit (CNN+GRU), have been compared. The results have shown improvement, reduction of errors, and a significant increase in the accuracy of the proposed method in classifying congestive heart failure from other arrhythmias and normal heart rhythms.