Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/36932
Title: | Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring | Authors: | Rozo, Andrea Moeyersons, Jonathan Morales, John Garcia van der Westen, Roberto Lijnen, Lien SMEETS, Christophe Jantzen, Sjors Monpellier, Valerie RUTTENS, David Van Hoof, Chris Van Huffel, Sabine Groenendaal, Willemijn Varon, Carolina |
Issue Date: | 2022 | Publisher: | Source: | Frontiers in Bioengineering and Biotechnology, 10 (Art N° 806761) | Abstract: | Changes in respiratory rate have been found to be one of the early signs of health deterioration in patients. In remote environments where diagnostic tools and medical attention are scarce, such as deep space exploration, the monitoring of the respiratory signal becomes crucial to timely detect life-threatening conditions. Nowadays, this signal can be measured using wearable technology; however, the use of such technology is often hampered by the low quality of the recordings, which leads more often to wrong diagnosis and conclusions. Therefore, to apply these data in diagnosis analysis, it is important to determine which parts of the signal are of sufficient quality. In this context, this study aims to evaluate the performance of a signal quality assessment framework, where two machine learning algorithms (support vector machine-SVM, and convolutional neural network-CNN) were used. The models were pre-trained using data of patients suffering from chronic obstructive pulmonary disease. The generalization capability of the models was evaluated by testing them on data from a different patient population, presenting normal and pathological breathing. The new patients underwent bariatric surgery and performed a controlled breathing protocol, displaying six different breathing patterns. Data augmentation (DA) and transfer learning (TL) were used to increase the size of the training set and to optimize the models for the new dataset. The effect of the different breathing patterns on the performance of the classifiers was also studied. The SVM did not improve when using DA, however, when using TL, the performance improved significantly (p < 0.05) compared to DA. The opposite effect was observed for CNN, where the biggest improvement was obtained using DA, while TL did not show a significant change. The models presented a low performance for shallow, slow and fast breathing patterns. These results suggest that it is possible to classify respiratory signals obtained with wearable technologies using pre-trained machine learning models. This will allow focusing on the relevant data and avoid misleading conclusions because of the noise, when designing bio-monitoring systems. | Keywords: | data augmentation;machine learning;respiratory monitoring;signal quality;transfer learning | Document URI: | http://hdl.handle.net/1942/36932 | ISSN: | 2296-4185 | e-ISSN: | 2296-4185 | DOI: | 10.3389/fbioe.2022.806761 | ISI #: | 000766542000001 | Rights: | Copyright © 2022 Rozo, Moeyersons, Morales, Garcia van der Westen, Lijnen, Smeets, Jantzen, Monpellier, Ruttens, Van Hoof, Van Huffel, Groenendaal and Varon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
fbioe-10-806761.pdf | Published version | 2.34 MB | Adobe PDF | View/Open |
WEB OF SCIENCETM
Citations
5
checked on Oct 12, 2024
Page view(s)
36
checked on Sep 7, 2022
Download(s)
10
checked on Sep 7, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.