Abstract
The capability to self-monitor and to be informed of health, empower people in preventing the spread of COVID-19 pandemic and improve health outcomes. Most of infected people are asymptomatic and they are able to spread the virus so detecting early the COVID-19 infection onset gives two advantages, it limits virus spreading and it limits the disease damages to the person. Vital signs measurements (heart rate, blood oxygen saturation, respiratory rate, heart rate variability, body temperature, electro dermal activity, motion activity) could be fused to infer about COVID-19 positive or COVID-19 negative person’s status at least one week in advance. Due to the fuzziness of measurements a softcomputing approach is requested, performing better than hardcomputing approach. Fuzzy-neuro inference paradigms, mainly the Evolving Connectionist Systems ECOS has the required capabilities to fit well such application. A wearable device (wrist) for vital signs data logging and measurement was been deployed and wireless connected to a host computing platform to run a fuzzy-neuro inference paradigm, the Evolving Fuzzy Neural Network (EFuNN) trained and tested on a dataset built by vital signs measurements. Test results confirmed the fast and effective learning capability of EfuNN paradigm. Better performance was been achieved applying the EFuNN’s evolving and online learning capabilities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Herrmann, J., Mori, V., Bates, J.H.T., et al.: Modeling lung perfusion abnormalities to explain early COVID-19 hypoxemia. Nat. Commun. 11, 4883 (2020). https://doi.org/10.1038/s41467-020-18672-6
Teo, J.: Early detection of silent hypoxia in COVID-19 pneumonia using smartphone pulse oximetry. J. Med. Syst. 44(8), 1–2 (2020). https://doi.org/10.1007/s10916-020-01587-6
Malcangi, M., Nano, G.: Biofeedback: e-health prediction based on evolving fuzzy neural network and wearable technologies. Evol. Syst. (2021). https://doi.org/10.1007/s12530-021-09374-5
Natarajan, A., Su, H.W., Heneghan, C.: Assessment of physiological signs associated with COVID-19 measured using wearable devices. NPJ Digit. Med. 3(1), 1–8 (2020). https://doi.org/10.1038/s41746-020-00363-7
Malcangi, M.: AI-based methods and technologies to develop wearable devices for prosthetics and predictions of degenerative diseases. In: Cartwright, H. (ed.) Artificial Neural Networks. Methods in Molecular Biology, vol. 2190, pp. 337–354. Springer, New York (2021). https://doi.org/10.1007/978-1-0716-0826-5_17
Palhmi, J., Broeders, J.H.: Optical integration without compromises (1995). https://www.analog.com/en/technical-articles/optical-integration-without-compromises.html
Broeders, J.: Wearable electronic devices monitor vital signs, activity level, and more. Anal. Dialog. 48(4) (2014)
Hunasikatti, M.: Racial bias in accuracy of pulse oximetry and its impact on assessments of hypopnea and T90 in clinical studies. J. Clin. Sleep Med. 17(5), 1145 (2021)
Colvonen, P.J.: Response to: investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digit. Med. 4(1), 1–36 (2021)
Kasabov, N.: EFuNN. IEEE Tr SMC (2001)
Kasabov, N.: Evolving Connectionist Systems: The Knowledge Engineering Approach. Springer, London (2007). https://doi.org/10.1007/978-1-84628-347-5
https://kedri.aut.ac.nz/areas-of-expertise/data-mining-and-decision-support/neucom. Accessed 12 Mar 2021
de Campos Souza, P.V.: Fuzzy neural networks and neuro-fuzzy networks: a review the main techniques and applications used in the literature. Appl. Soft Comput. 92, 106275 (2020). https://doi.org/10.1016/j.asoc.2020.106275
Skrjanc, I., Iglesias, J.A., Sanchis, A., Leite, D., Lughofer, E., Gomide, F.: Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: a Survey. Inf. Sci. 490, 344–368 (2019). https://doi.org/10.1016/j.ins.2019.03.060
Yolery, Y., Bu, G.: Multiparameter vital signs monitoring is easier than before. Anal Dial. 54(4) (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Malcangi, M. (2021). Early Prediction of COVID-19 Onset by Fuzzy-Neuro Inference. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-80568-5_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80567-8
Online ISBN: 978-3-030-80568-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)