Classification of cardiac arrhythmia using a convolutional neural network and bi-directional long short-term memory

Abstract

Cardiac arrhythmia is a leading cause of cardiovascular disease, with a high fatality rate worldwide. The timely diagnosis of cardiac arrhythmias, determined by irregular and fast heart rate, may help lower the risk of strokes. Electrocardiogram signals have been widely used to identify arrhythmias due to their non-invasive approach. However, the manual process is error-prone and time-consuming. A better alternative is to utilize deep learning models for early automatic identification of cardiac arrhythmia, thereby enhancing diagnosis and treatment. In this article, a novel deep learning model, combining convolutional neural network and bi-directional long short-term memory, is proposed for arrhythmia classification. Specifically, the classification comprises five different classes: non-ectopic (N), supraventricular ectopic (S), ventricular ectopic (V), fusion (F), and unknown (Q) beats. The proposed model is trained, validated, and tested using MIT-BIH and St-Petersburg data sets separately. Also, the performance was measured in terms of precision, accuracy, recall, specificity, and f1-score. The results show that the proposed model achieves training, validation, and testing accuracies of 100%, 98%, and 98%, respectively with the MIT-BIH data set. Lower accuracies were shown for the St-Petersburg data set. The performance of the proposed model based on the MIT-BIH data set is also compared with the performance of existing models based on the MIT-BIH data set.

Cardiac abnormalities from 12‐Lead ECG signals prediction based on deep convolutional neural network optimized with nomadic people optimization algorithm

“…Hassan et al, 22 have presented the cardiac arrhythmia categorization depending on convolutional neural network along bi‐directional long short‐term memory. The deep learning model for classifying cardiac arrhythmias combines bidirectional long short‐term memory with convolutional neural networks.…”

Machine Learning for Cardiac Arrhythmia Detection: A Systematic Survey

“…Hassan SU et al [28] proposed an automated model for the efficient classification of cardiac arrhythmia. They used bi-directional LSTM and CNN techniques for the detection of cardiac arrhythmia.…”

“…It would aid medical professionals in early disease diagnosis and appropriate treatment administration to lower the risk of problems. Researchers employ a variety of machine learning methods, including Deep Neural Network [21,28], Convolutional Neural Network [34], Decision Tree [20,31], logistic regression [30], SVM [16,26,27,32], K-NN [15], ANN [17] etc. for the detection of cardiac arrhythmia.…”

Implementation of an Artificial Intelligence-Based Ecg Acquisition System for the Detection of Cardiac Abnormalities

“…Convolutional neural networks are similar to artificial neural networks in that they have a convolution layer, a subsampling layer, and a fully connected layer that is the same as the multilayer perceptron (MLP). In [8], they introduced an automated feature extraction method that eliminates the need for human feature extraction and preprocessing. It focuses on identifying five major macroclasses: Non-ectopic, Supraventricular ectopic (S), Ventricular ectopic (V), Fu-sion (F), and Unknown (Q).…”