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)