Authors:
Lana Dominković
1
;
Biljana Mileva Boshkoska
1
;
2
and
Aleksandra Rashkovska
1
;
2
Affiliations:
1
Faculty of Information Studies, Ljubljanska cesta 31a, Novo Mesto, Slovenia
;
2
Jožef Stefan Institute, Jamova cesta 39, Ljubljana, Slovenia
Keyword(s):
ECG-Derived Respiration, Biosignal Analysis, Deep Learning, Signal Processing, Convolutional Autoencoder, Fantasia, Respiratory Signal Estimation, Healthcare Monitoring.
Abstract:
In this paper, we explore a deep learning approach for extracting respiratory signals from electrocardiogram (ECG) data using the Fantasia dataset. We implemented a fully convolutional neural network model, inspired by the U-Net architecture, and designed to estimate respiratory signals from ECG data. The model incorporates convolutional layers, ReLU activations, batch normalization, max pooling, and up-sampling layers. Our deep learning model achieved an average correlation coefficient (CC) of 0.51 and Mean Squared Error (MSE) of 0.046, outperforming four out of six baseline signal processing algorithms based on the CC metric, and outperforming all signal processing algorithms based on the MSE metric. These findings demonstrate the effectiveness of deep learning in improving the accuracy and robustness of ECG-derived respiration (EDR). The research highlights the potential of advanced machine learning models for non-invasive respiratory monitoring and paves the way for future studie
s focused on exploring more complex architectures and broader datasets to further enhance performance and generalizability.
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