Convolutional transformer-driven robust electrocardiogram signal denoising framework with adaptive parametric ReLU

• Published in: Mathematical biosciences and engineering : MBE Vol. 21; no. 3; pp. 4286 - 4308
• Main Authors: Wang, Jing, Pei, Shicheng, Yang, Yihang, Wang, Huan
• Format: Journal Article
• Language: English
• Published: United States AIMS Press 01.01.2024
• Subjects: convolutional neural network; ecg signal; signal denoising; transformer; signal denoising; transformer; ECG signal; convolutional neural network
• DOI: 10.3934/mbe.2024189
• ISSN: 1551-0018

The electrocardiogram (ECG) is a widely used diagnostic tool for cardiovascular diseases. However, ECG recording is often subject to various noises, which can limit its clinical evaluation. To address this issue, we propose a novel Transformer-based convolutional neural network framework with adaptively parametric ReLU (APtrans-CNN) for ECG signal denoising. The proposed APtrans-CNN architecture combines the strengths of transformers in global feature learning and CNNs in local feature learning to address the inadequacy of learning with long sequence time-series features. By fully exploiting the global features of ECG signals, our framework can effectively extract critical information that is necessary for signal denoising. We also introduce an adaptively parametric ReLU that can assign a value to the negative information contained in the ECG signal, thereby overcoming the limitation of ReLU to retain negative information. Additionally, we introduce a dynamic feature aggregation module that enables automatic learning and retention of valuable features while discarding useless noise information. Results obtained from two datasets demonstrate that our proposed APtrans-CNN can accurately extract pure ECG signals from noisy datasets and is adaptable to various applications. Specifically, when the input consists of ECG signals with a signal-to-noise ratio (SNR) of -4 dB, APtrans-CNN successfully increases the SNR to more than 6 dB, resulting in the diagnostic model's accuracy exceeding 96%.