City Research Online

The efficacy of support vector machines in modelling deviations from the Beer-Lambert law for optical measurement of lactate

Mamouei, M. H., Budidha, K. ORCID: 0000-0002-6329-8399, Baishya, N. ORCID: 0000-0002-2231-6132, Qassem, M. ORCID: 0000-0003-0730-3189 and Kyriacou, P. A. ORCID: 0000-0002-2868-485X (2020). The efficacy of support vector machines in modelling deviations from the Beer-Lambert law for optical measurement of lactate. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020(July), pp. 4261-4264. doi: 10.1109/EMBC44109.2020.9175215 ISSN 1557-170X

Abstract

Lactate is an important biomarker with a significant diagnostic and prognostic ability in relation to life-threatening conditions and diseases such as sepsis, diabetes, cancer, pulmonary and kidney diseases, to name a few. The gold standard method for the measurement of lactate relies on blood sampling, which due to its invasive nature, limits the ability of clinicians in frequent monitoring of patients' lactate levels. Evidence suggests that the optical measurement of lactate holds promise as an alternative to blood sampling. However, achieving this aim requires better understanding of the optical behavior of lactate. The present study investigates the potential deviations of absorbance from the Beer-Lambert law in high concentrations of lactate. To this end, a number of nonlinear models namely support vector machines with quadratic, cubic and quartic kernels and radial basis function kernel are compared with the linear principal component regression and linear support vector machine. Interestingly, it is shown that even in extremely high concentrations of lactate (600 mmol/L) in a phosphate buffer solution, the linear models surpass the performance of the other models.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Support vector machines, Kernel, Nonlinear optics, Blood, Adaptive optics, Optical scattering, Optical sensors
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
Date Deposited: 08 Oct 2020 14:16
URI: https://openaccess.city.ac.uk/id/eprint/25047
[img]
Preview
Text - Accepted Version
Download (412kB) | Preview

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login