  Electrocardiogram (ECG) signals play a pivotal role in cardiovascular
diagnostics, providing essential information on the electrical activity of the
heart. However, the inherent noise and limited resolution in ECG recordings can
hinder accurate interpretation and diagnosis. In this paper, we propose a novel
model for ECG super resolution (SR) that uses a DNAE to enhance temporal and
frequency information inside ECG signals. Our approach addresses the
limitations of traditional ECG signal processing techniques. Our model takes in
input 5-second length ECG windows sampled at 50 Hz (very low resolution) and it
is able to reconstruct a denoised super-resolution signal with an x10
upsampling rate (sampled at 500 Hz). We trained the proposed DCAE-SR on public
available myocardial infraction ECG signals. Our method demonstrates superior
performance in reconstructing high-resolution ECG signals from very
low-resolution signals with a sampling rate of 50 Hz. We compared our results
with the current deep-learning literature approaches for ECG super-resolution
and some non-deep learning reproducible methods that can perform both
super-resolution and denoising. We obtained current state-of-the-art
performances in super-resolution of very low resolution ECG signals frequently
corrupted by ECG artifacts. We were able to obtain a signal-to-noise ratio of
12.20 dB (outperforms previous 4.68 dB), mean squared error of 0.0044
(outperforms previous 0.0154) and root mean squared error of 4.86% (outperforms
previous 12.40%). In conclusion, our DCAE-SR model offers a robust (to artefact
presence), versatile and explainable solution to enhance the quality of ECG
signals. This advancement holds promise in advancing the field of
cardiovascular diagnostics, paving the way for improved patient care and
high-quality clinical decisions
