Attention-Based Convolutional Denoising Autoencoder for Two-Lead ECG Denoising and Arrhythmia Classification

This article presents a fast and accurate electrocardiogram (ECG) denoising and classification method for low-quality ECG signals. To achieve this, a novel attention-based convolutional denoising autoencoder (ACDAE) model is proposed that utilizes a skip-layer and attention module for reliable reconstruction of ECG signals from extreme noise conditions. Skip-layer connections are used to reduce information loss while reconstructing the original signal, and a lightweight, efficient channel attention (ECA) module is used to update relevant features retrieved via cross-channel interaction efficiently. The model is trained and tested using four widely available databases. For evaluation, the signals are mixed with simulated additive white Gaussian noise (AWGN) ranging from −20 to 20 dB and the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) noise stress test database (NSTDB) noise ranging from −6 to 24 dB. The model outperformed the most cited published works, achieving an average signal-to-noise ratio (SNR) improvement of 19.07 ± 1.67 and a percentage-root-mean-square difference (PRD) of 11.0 % at 0-dB SNR. The model classification performance on 60 000 beats is 98.76% ± 0.44% precision, 98.48% ± 0.58% recall, and 98.88% ± 0.42% accuracy, respectively, using a stratified fivefold cross-validation strategy.