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Improved electrode motion artefact denoising in ECG using convolutional neural networks and a custom loss function

Brophy, Eoin, Hennelly, Bryan orcid logoORCID: 0000-0003-1326-9642, De Vos, Maarten orcid logoORCID: 0000-0002-3482-5145, Boylan, Geraldine orcid logoORCID: 0000-0003-0920-5291 and Ward, Tomás E. orcid logoORCID: 0000-0002-6173-6607 (2022) Improved electrode motion artefact denoising in ECG using convolutional neural networks and a custom loss function. IEEE Access, 10 . pp. 54891-54898. ISSN 2169-3536

Heart disease is the leading cause of mortality worldwide, and it is of utmost importance that clinicians and researchers understand the dynamics of the heart. As an electrical measure of the heart’s activity, the electrocardiogram, or ECG, is the gold standard for recording the cardiac state, whether monitoring the structure of the traces that make up the ECG or indicating key metrics such as heart rate variability. Long-term monitoring of ECG is often required to identify cardiovascular issues but proves impractical; therefore, patients will remotely collect their data. However, ECG signals can become contaminated with various noise sources during data collection. This paper proposes a custom loss function capable of denoising electrode motion artefact in ECG data to a higher standard than other, more common loss functions. We implement our custom loss function with a convolutional neural network to return high-quality ECG, suitable for calculating the aforementioned key metrics from a previously unobtainable state. The proposed model improves ECG signals overall signal-to-noise ratio and preserves the R waves structure. The model outperforms a standard mean squared error loss function with an improvement of 0.5 dB in terms of signal to noise ratio and improves the heart rate estimation by 25%.
Item Type:Article (Published)
Uncontrolled Keywords:Convolutional neural network; custom loss function; electrocardiography; signal denoising.
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Official URL:https://dx.doi.org/10.1109/ACCESS.2022.3176971
Copyright Information:© 2021 The Authors.
Funders:Science Foundation Ireland under Grant 17/RC-PhD/3482 and Grant SFI/12/RC/2289_P2, Flemish Government (AI Research Program)
ID Code:27540
Deposited On:11 Aug 2022 15:50 by Thomas Murtagh . Last Modified 15 Mar 2023 15:05

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