Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques

Abstract EN

Although convolutional neural networks (CNNs) can be used to classify electrocardiogram (ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed as one-dimensional signals while CNNs are better suited to multidimensional pattern or image recognition applications.

In this study, the morphology and rhythm of heartbeats are fused into a two-dimensional information vector for subsequent processing by CNNs that include adaptive learning rate and biased dropout methods.

The results demonstrate that the proposed CNN model is effective for detecting irregular heartbeats or arrhythmias via automatic feature extraction.

When the proposed model was tested on the MIT-BIH arrhythmia database, the model achieved higher performance than other state-of-the-art methods for five and eight heartbeat categories (the average accuracy was 99.1% and 97%).

In particular, the proposed system had better performance in terms of the sensitivity and positive predictive rate for V beats by more than 4.3% and 5.4%, respectively, and also for S beats by more than 22.6% and 25.9%, respectively, when compared to existing algorithms.

It is anticipated that the proposed method will be suitable for implementation on portable devices for the e-home health monitoring of cardiovascular disease.