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

This paper 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 model is proposed that utilizes 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 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 ranging from -20 to 20 decibels (dB) and 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 percentage-root-mean-square difference (PRD) of 11.0% at 0 dB SNR noise. The model classification performance on 60, 000 beats are 98.76 ± 0.44% precision, 98.48 ± 0.58% recall, and 98.88 ± 0.42% accuracy, respectively using a stratified five-fold cross-validation strategy.