Classifying Cardiac Arrhythmia from ECG Signal Using 1D CNN Deep Learning Model

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Abstract

Blood circulation depends critically on electrical activation, where any disturbance in the orderly pattern of the heart’s propagating wave of excitation can lead to arrhythmias. Diagnosis of arrhythmias using electrocardiograms (ECG) is widely used because they are a fast, inexpensive, and non-invasive tool. However, the randomness of arrhythmic events and the susceptibility of ECGs to noise leads to misdiagnosis of arrhythmias. In addition, manually diagnosing cardiac arrhythmias using ECG data is time-intensive and error-prone. With better training, deep learning (DL) could be a better alternative for fast and automatic classification. The present study introduces a novel deep learning architecture, specifically a one-dimensional convolutional neural network (1D-CNN), for the classification of cardiac arrhythmias. The model was trained and validated with real and noise-attenuated ECG signals from the MIT-BIH dataset. The main aim is to address the limitations of traditional electrocardiograms (ECG) in the diagnosis of arrhythmias, which can be affected by noise and randomness of events, leading to misdiagnosis and errors. To evaluate the model performance, the confusion matrix is used to calculate the model accuracy, precision, recall, f1 score, average and AUC-ROC. The experiment results demonstrate that the proposed model achieved outstanding performance, with 1.00 and 0.99 accuracies in the training and testing datasets, respectively, and can be a fast and automatic alternative for the diagnosis of arrhythmias.

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