Details

Autor(en) / Beteiligte

• Rangaiah, Pramod K B
• Kouki, Mokhtar
• Dhouibi, Yasmina
• Huss, Fredrik
• Mandal, Bappaditya
• Augustine, Bobins
• Perez, Mauricio David
• Augustine, Robin

Titel

Dielectric Characterization and Statistical Analysis of Ex-Vivo Burnt Human Skin Samples For Microwave Sensor Development

Ist Teil von

• IEEE access, 2023-01, Vol.11, p.1-1

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2023

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• The dielectric properties of skin tissues in relation to different degrees of burn are a necessary prerequisite for designing non-invasive microwave sensing modalities. Due to the difficulties in obtaining human tissue samples, such databases are largely unavailable. To bridge the knowledge gap in this field, we attempt to create a dielectric database of various burn-degree skin samples and their statistical analysis in this work. This research is part of the European "Senseburn" project, which aims to create a non-invasive diagnostic tool that can measure the severity and depth of burns on humans in a clinical setting. In this work, several ex-vivo burnt samples were collected from the Uppsala University Hospital (Akademiska sjukhuset, Sweden). Out of that, eight samples with different degrees of burns in various human body locations were selected for the analysis. The dielectric characterization of the categorized samples was done using an Keysight N1501A dielectric open-end co-axial probe Kit. The dielectric characterization was made from 500 MHz to 10 GHz with 1001 points. The measurement was made systematically, and the clinician feedback forms were gathered and analyzed throughout the process. The measurement data followed the FASTCLUS procedure, which was initially analyzed using density plot, convergence, and cubic clustering criteria. For the statistical analysis, 11 frequency points were considered for eight samples. The results of the fundamental statistical analysis using the FASTCLUS procedure resulted in 88 data sets. Later, data sets were analyzed in sample-wise clusters. Every sample was made with two clusters, i.e., cluster 1, which consisted of healthy sectors, and cluster 2, which consisted of burnt sectors. We made the linear approximations for the sample-wise clusters and found the constant real permittivity difference. Furthermore, we found a pattern in the constant real permittivity differences of every sample that is proportional to the burn degrees. This information is needed in order to identify optimization parameters, i.e., the sensitivity with respect to dielectric difference for various burn degrees. For this purpose, extensive measurement campaigns across the microwave frequency band from 500 MHz - 10 GHz were conducted. Based on the analysis of dielectric data, each skin region of interest (ROI) has its own dielectric properties. Additionally, we developed a proof of concept non-invasive flexible microwave sensor based on the dielectric database collected from burnt ex-vivo human tissue samples. In this way, we could distinguish between phantoms with different dielectric properties in the burned human tissue sample range.