BlendNet: A Blending-based Convolutional Neural Network for Effective Deep Learning of Electrocardiogram Signals

Premanand SSathiya Narayanan Sekar^*

• School of Electronics Engineering, Vellore Institute of Technology, Chennai, Chennai, India

In recent years, Deep Learning (DL) architectures such as Convolutional Neural Network (CNN) and its variants are shown to be effective in the diagnosis of cardiovascular disease from ElectroCardioGram (ECG) signals. In the case of ECG as a one-dimensional signal, 1-D CNNs are deployed whereas in the case of a 2D-represented ECG signal, i.e. two-dimensional signal, 2-D CNNs or other relevant architectures are deployed. Since 2D-represented ECG signals facilitate better feature extraction, it is a common practice to convert an ECG signal intoa scalogram image using a continuous wavelet transform (CWT) approach and then subject it to a DL architecture such as 2-D CNN. However, this traditional approach captures only a limited set of features of ECG and thereby limits the effectiveness of DL architectures in disease detection. This work proposes 'BlendNet', a DL architecture that effectively extracts the features of an ECG signal using a blending approach termed 'alpha blending'. First the 1-D ECG signal is converted into a scalogram image using CWT, and a binary version of the scalogram image is also obtained. Then, both the scalogram and binary images are subjected to a sequence of convolution and pooling layers, and the resulting feature images are blended. This blended feature image is subjected to a dense layer that classifies the image. The blending is flexible, and it is controlled by a parameter α, and hence the process is termed as alpha blending. The utilisation of alpha blending facilitates the generation of a composite feature set that incorporates different characteristics from both the scalogram and binary versions. For experiments, a total of 162 ECG recordings from the PhysioNet database were used. Experimental results and analysis show that, in the case of α = 0.7, BlendNet's performance surpasses the performance of (i) traditional approaches (that doesn't involve blending) and (ii) state-of-the-art approaches for ECG classification. In addition, experimental outcomes show that the proposed BlendNet is flexible regarding dense layer settings and can accommodate faster alternatives (i.e., machine learning (ML) algorithms) for faster convergence.