Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN

Bojana Koteska, Ana Bogdanova, Teodora Vićentić, Stefan Ilić, Miona Tomić, Marko Spasenović

2024

Abstract

This paper explores the feasibility of using wearable laser-induced graphene (LIG) sensors to estimate oxygen saturation (SpO2) as an alternative to traditional photoplethysmography (PPG) oximeters, particularly in mass casualty triage scenarios. Positioned on the chest, the LIG sensor continuously monitors respiratory signals in real-time. The study leverages deep neural network (DNN) trained on PPG signals to process LIG respiratory signals, revealing promising results. Key performance metrics include a mean squared error (MSE) of 0.152, a mean absolute error (MAE) of 1.13, a root mean square error (RMSE) of 1.23, and an R2 score of 0.68. This innovative approach, combining PPG and respiratory signals from graphene, offers a potential solution for 2D sensors in emergency situations, enhancing the monitoring and management of various medical conditions. However, further investigation is required to establish the clinical applications and correlations between these signals. This study marks a significant step toward advancing wearable sensor technology for critical healthcare scenarios.

Paper Citation

in Harvard Style

Koteska B., Bogdanova A., Vićentić T., Ilić S., Tomić M. and Spasenović M. (2024). Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-688-0, SciTePress, pages 739-746. DOI: 10.5220/0012354100003657

in Bibtex Style

@conference{biosignals24,
author={Bojana Koteska and Ana Bogdanova and Teodora Vićentić and Stefan Ilić and Miona Tomić and Marko Spasenović},
title={Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2024},
pages={739-746},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012354100003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN
SN - 978-989-758-688-0
AU - Koteska B.
AU - Bogdanova A.
AU - Vićentić T.
AU - Ilić S.
AU - Tomić M.
AU - Spasenović M.
PY - 2024
SP - 739
EP - 746
DO - 10.5220/0012354100003657
PB - SciTePress