Abstract
The electrocardiogram (ECG) is an established exam to diagnose cardiovascular disease. Due to the increasing popularity of wearables, a wide part of the population has now access to (self-)monitorization of cardiovascular activity. Wearable ECG acquisition systems are prone to noise sources stemming from surrounding muscle activation, electrode movement, and baseline wander. Hence, many attempts have been made to develop algorithms that clean the signal, but their performance falls short when applied to very noisy signals. Acknowledging the demonstrated power of Deep Learning on timeseries processing, we propose a ECG denoiser based on Gated Recurrent Units (GRU). Noisy ECG samples were created by adding noise from the MIT-BIH Noise Stress Test database to ECG samples from the PTB-XL database. The trained network proves to remove various common noise types resulting in high quality ECG signals, while having a much smaller number of parameters compared to state-of-the-art DL approaches.
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Acknowledgments
This work was supported by Project OPERATOR (NORTE01-0247-FEDER-045910), cofinanced by the European Regional Development Fund through the North Portugal Regional Operational Program and Lisbon Regional Operational Program and by the Portuguese Foundation for Science and Technology, under the MIT Portugal Program. M. Dias and P. Probst were supported by the doctoral Grants SFRH/BD/151375/2021 and RT/BD/152843/2021, respectively, financed by the Portuguese Foundation for Science and Technology (FCT), and with funds from State Budget, under the MIT Portugal Program.
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Dias, M., Probst, P., Silva, L., Gamboa, H. (2023). Cleaning ECG with Deep Learning: A Denoiser Based on Gated Recurrent Units. In: Camarinha-Matos, L.M., Ferrada, F. (eds) Technological Innovation for Connected Cyber Physical Spaces. DoCEIS 2023. IFIP Advances in Information and Communication Technology, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-031-36007-7_11
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DOI: https://doi.org/10.1007/978-3-031-36007-7_11
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