Does a Denoising Convolutional Autoencoder improve the reconstruction of high-resolution ECG signals from low-resolution inputs compared to state-of-the-art methods?
A novel Denoising Convolutional Autoencoder effectively reconstructs high-resolution ECG signals from low-resolution inputs, outperforming existing methods and potentially aiding in the detection of heart-related diseases.
Electrocardiogram signals play a pivotal role in cardiovascular diagnostics, providing essential information on electrical hearth activity. However, inherent noise and limited resolution can hinder an accurate interpretation of the recordings. In this paper an advanced Denoising Convolutional Autoencoder designed to process electrocardiogram signals, generating super-resolution reconstructions is proposed; this is followed by in-depth analysis of the enhanced signals. The autoencoder receives a signal window (of 5 s) sampled at 50 Hz (low resolution) as input and reconstructs a denoised super-resolution signal at 500 Hz. The proposed autoencoder is applied to publicly available datasets, demonstrating optimal performance in reconstructing high-resolution signals from very low-resolution inputs sampled at 50 Hz. The results were then compared with current state-of-the-art for electrocardiogram super-resolution, demonstrating the effectiveness of the proposed method. The method achieves a signal-to-noise ratio of 12.20 dB, a mean squared error of 0.0044, and a root mean squared error of 4.86%, which significantly outperforms current state-of-the-art alternatives. This framework can effectively enhance hidden information within signals, aiding in the detection of heart-related diseases. • We defined a novel architecture based on autocencoders which is able to denoise and reconstruct high resolution copies of input low resolution ECG signals. • This unique approach that has not been previously applied to ECG signals. • We also present a deep validation of our approach against traditional and contemporary methods in terms of signal-to-noise ratio, mean squared error, and root mean squared error with those of other widely used ECG signal processing techniques. • The results consistently showed superior performance, further validating the effectiveness of our approach. • Given the increasing reliance on effective and efficient diagnostic techniques in medical practice, especially in cardiology, the findings of our study have significant practical implications.
Lomoio et al. (Sat,) studied this question.
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