abstract

• Tissular blood perfusion is helpful to assess the health condition of a subject and even monitor superficial lesions. Current state of the art is focused on developing non-invasive, quantitative and accessible methods for blood flow monitoring in large areas. This paper presents an approach based on multispectral images on the VIS-NIR range to quantify blood perfusion. Our goal is to estimate the changes in deoxygenated hemoglobin. To do so, we employ principal component analysis followed by a linear regression model. The proposal was evaluated using in-vivo data from a vascular occlusion protocol, and the results were validated against photoplethysmography measurements. Although the number of subjects in the protocol was limited, our model made a prediction with an average similarity of 91.53%25 with a mean R-squared adjusted of 0.8104. © 2021 IEEE.

authors

• Campos Delgado Daniel Ulises
• Gutierrez-Navarro O.

publication date

• 2021-01-01

funding provided via

• Consejo Nacional de Ciencia y Tecnología, CONACYT: 254637  Grant

published in

• Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS  Proceedings

keywords

• hemoglobin; diagnostic imaging; human; principal component analysis; Diagnostic Imaging; Hemoglobins; Humans; Principal Component Analysis

Digital Object Identifier (DOI)

• 10.1109/EMBC46164.2021.9629574

PubMed ID

start page

• 3161

end page

• 3164

volume

issue