Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

Abstract

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.

Deep-Learning Premature Contraction Localization in 12-lead ECG From Whole Signal Annotations

“…The accuracy is comparable with the results of another recent studies with CNN classification (e.g. 0.979 [13], 0.979 [14]).…”

From ECG signals to images: a transformation based approach for deep learning

“…For example, one system achieved an accuracy of 94.07% and 91.5% on the MIT-BIH dataset (Acharya et al, 2017); moreover, they are also time-consuming. The latest technique, which included a deep CNN to make the algorithm automated, gained a higher accuracy of 97.6% on a similar dataset (Ullah et al, 2020). Ullah et al (2020) Algorithm (GA), for ECG beat classification, an achieved an accuracy of 97.7% on the MIT-BIH dataset.…”

“…The latest technique, which included a deep CNN to make the algorithm automated, gained a higher accuracy of 97.6% on a similar dataset (Ullah et al, 2020). Ullah et al (2020) Algorithm (GA), for ECG beat classification, an achieved an accuracy of 97.7% on the MIT-BIH dataset. Yang et al (2021) proposed an ensemble multiclass classifier that combined mixed-kernel-based extreme learning machine (MKELM) as base learner and random forest as a meta-learner, achieving an overall accuracy of 98.1% in classifying four types of heartbeats.…”

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

“…Deep Learning, such as Artificial Neural Networks (ANN), is successfully applied in applications, such as information retrieval [ 28 ], image recognition, object tracking, and language processing [ 29 ]. In Reference [ 30 ], a two-dimensional convolution neural network (2D CNN) model is developed to classify the ECG signals for the diagnosis of arrhythmia. However, in Reference [ 30 ], only a 2D CNN method is applied, while the 1D CNN model is completely ignored.…”