Premanand S, Narayanan Sathiya
School of Electronics Engineering (SENSE), Vellore Institute of Technology, Chennai, India.
Front Artif Intell. 2025 Aug 22;8:1625637. doi: 10.3389/frai.2025.1625637. eCollection 2025.
In recent years, Deep Learning (DL) architectures such as Convolutional Neural Network (CNN) and its variants have been 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 into a 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, hence the process is termed as alpha blending. The utilization 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 do not involve blending) and (ii) state-of-the-art approaches for ECG classification.
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. The superior performance at α = 0.7 indicates that alpha blending allows for richer composite feature sets, leading to improved classification accuracy over conventional feature extraction and classification methods.
近年来，诸如卷积神经网络（CNN）及其变体等深度学习（DL）架构已被证明在从心电图（ECG）信号诊断心血管疾病方面是有效的。对于作为一维信号的心电图，部署一维卷积神经网络；而对于二维表示的心电图信号，即二维信号，则部署二维卷积神经网络或其他相关架构。由于二维表示的心电图信号有助于更好地进行特征提取，因此通常的做法是使用连续小波变换（CWT）方法将心电图信号转换为尺度图图像，然后将其应用于二维卷积神经网络等深度学习架构。然而，这种传统方法仅捕获了心电图的有限特征集，从而限制了深度学习架构在疾病检测中的有效性。
这项工作提出了“融合网络”（BlendNet），这是一种深度学习架构，它使用一种称为“阿尔法融合”的融合方法有效地提取心电图信号的特征。首先，使用连续小波变换将一维心电图信号转换为尺度图图像，并获得尺度图图像的二进制版本。然后，将尺度图和二进制图像都经过一系列卷积和池化层，并对得到的特征图像进行融合。这个融合后的特征图像经过一个全连接层进行图像分类。融合是灵活的，由参数α控制，因此该过程被称为阿尔法融合。阿尔法融合的使用有助于生成一个包含来自尺度图和二进制版本不同特征的复合特征集。
在实验中，总共使用了来自PhysioNet数据库的162份心电图记录。实验结果和分析表明，在α = 0.7的情况下，融合网络的性能超过了（i）传统方法（不涉及融合）和（ii）心电图分类的现有最先进方法。
实验结果表明，所提出的融合网络在全连接层设置方面具有灵活性，并且可以采用更快的替代方法（即机器学习算法）以实现更快的收敛。α = 0.7时的卓越性能表明，阿尔法融合允许生成更丰富的复合特征集，从而比传统的特征提取和分类方法提高分类准确率。
