Show simple item record

AuthorChowdhury, Moajjem H.
AuthorShuzan, Md N.
AuthorChowdhury, Muhammad E. H.
AuthorReaz, Mamun B.
AuthorMahmud, Sakib
AuthorAl Emadi, Nasser
AuthorAyari, Mohamed A.
AuthorAli, Sawal H.
AuthorBakar, Ahmad Ashrif A.
AuthorRahman, Syed M.
AuthorKhandakar, Amith
Available date2023-04-17T06:57:42Z
Publication Date2022
Publication NameBioengineering
ResourceScopus
URIhttp://dx.doi.org/10.3390/bioengineering9100558
URIhttp://hdl.handle.net/10576/41942
AbstractRespiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients. 2022 by the authors.
SponsorThis work was made possible by NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, a member of Qatar Foundation, Doha, Qatar and Grant numbers DIP-2020-004 and GUP-2021-019 from Universiti Kebangsaan Malaysia. The statements made herein are solely the responsibility of the authors.
Languageen
PublisherMDPI
SubjectConvMixer
convolutional neural networks
deep learning
machine learning
photoplethysmogram
respiration rate
TitleLightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal
TypeArticle
Issue Number10
Volume Number9
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record