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Data Augmentation, Multimodality, Subject and Activity Specificity Improve Wearable Electrocardiogram Denoising with Autoencoders

Topics: Cardiovascular Signals; Medical Signal Acquisition, Analysis and Processing; Neural Networks for Biosignal Data; Pattern Recognition & Machine Learning for Biosignal Data; Wearable Sensors and Systems

Authors: João Saraiva 1 ; 2 ; 3 ; Mariana Abreu 1 ; 3 ; Ana Carmo 1 ; 3 ; Ana Fred 1 ; 3 and Hugo Plácido da Silva 1 ; 3

Affiliations: 1 Department of Bioengineering, Instituto Superior Técnico, Univeristy of Lisbon, Portugal ; 2 Department of Computer Science and Engineering, Instituto Superior Técnico, Univeristy of Lisbon, Portugal ; 3 Pattern and Image Analysis Group, Instituto de Telecomunicações, Portugal

Keyword(s): Biosignal Denoising, Electrocardiogram, Accelerometry, Ambulatory Wearables, Data Augmentation.

Abstract: Event detection based on biosignals continuously acquired by wearable devices has become an emergent topic. Particularly, real-time event detection with the electrocardiogram (ECG) has been explored to monitor heart conditions and epileptic seizures in the ambulatory. However, ECG acquired in the ambulatory is much more prone to noise and artifacts, due to the dynamic nature of these environments. Therefore, real-time and robust ECG denoising methods are crucial if event detection is meant to succeed. Denoising autoencoders (DAEs) are studied as robust and fast methods to attenuate ECG noise and artifacts. ECG data augmentation techniques are shown to effectively improve the performance of such a deep learning method. Activity and subject specific models are shown to output better ECG denoised estimates, than non-specific ones. And using accelerometry (ACC) as noise reference exemplifies how biosignal multimodality improves ECG attenuation of muscle and motion artifacts. Therefore, t his work establishes effective design techniques to be considered when engineering ECG deep learning models. (More)

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Paper citation in several formats:
Saraiva, J.; Abreu, M.; Carmo, A.; Fred, A. and Plácido da Silva, H. (2023). Data Augmentation, Multimodality, Subject and Activity Specificity Improve Wearable Electrocardiogram Denoising with Autoencoders. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS; ISBN 978-989-758-631-6; ISSN 2184-4305, SciTePress, pages 133-145. DOI: 10.5220/0011883400003414

@conference{biosignals23,
author={João Saraiva. and Mariana Abreu. and Ana Carmo. and Ana Fred. and Hugo {Plácido da Silva}.},
title={Data Augmentation, Multimodality, Subject and Activity Specificity Improve Wearable Electrocardiogram Denoising with Autoencoders},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS},
year={2023},
pages={133-145},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011883400003414},
isbn={978-989-758-631-6},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS
TI - Data Augmentation, Multimodality, Subject and Activity Specificity Improve Wearable Electrocardiogram Denoising with Autoencoders
SN - 978-989-758-631-6
IS - 2184-4305
AU - Saraiva, J.
AU - Abreu, M.
AU - Carmo, A.
AU - Fred, A.
AU - Plácido da Silva, H.
PY - 2023
SP - 133
EP - 145
DO - 10.5220/0011883400003414
PB - SciTePress