Deep Recurrent Neural Networks for Cardiac Arrhythmias Detection in Long-Term Ecg Analysis

Abstract

Arrhythmia, a severe type of heart disease, results from disruptions in the heart's electrical system, leading to irregular heartbeats. Electrocardiograms (ECGs) play a crucial role in monitoring and diagnosing heart conditions by recording the heart's electrical activity over time. They serve as the primary diagnostic tool for capturing and analyzing the heart's electrical activity, essential for early diagnosis and treatment planning. Arrhythmias, characterized by irregular heart rhythms, manifest in various forms of coronary heart disease. These irregularities occur when the heart's electrical impulses deviate from normal functioning, resulting in either tachycardia or bradycardia. The complexity of ECG waveforms makes manual detection of arrhythmias challenging for healthcare professionals. Consequently, deep learning techniques are increasingly applied for automatic detection, enabling timely clinical diagnosis and treatment. This paper explores methodologies for generating and processing ECG time series data using Python, focusing on two primary approaches: generating synthetic ECG signals and analyzing real ECG data through the MIT-BIH Arrhythmia Dataset. Various deep learning models, including Support Vector Machine (SVM), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN) classifiers, are implemented. They are compared to detect abnormalities in different types of heartbeats from arrhythmia ECG datasets. The RNN outperforms SVM, CNN, and LSTM models, achieving the highest accuracy of 99%, precision of 0.99, recall of 0.99, and F1-score of 0.99. This study underscores the importance of deep learning in improving the accuracy of arrhythmia detection and demonstrates the effectiveness of RNNs for this application.