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Impact of the human body in wireless propagation of medical implants for tumor detectionImpact of the human body in wireless propagation of medical implants for tumor detection

Other Titles
Impact of the human body in wireless propagation of medical implants for tumor detection
Authors
마누엘 에우제니오 모로초 카얌셀라김명식임완수
Issue Date
Feb-2020
Publisher
한국인터넷정보학회
Keywords
Body area networks; wireless implants; indoor propagation; fading estimation; human tissue modeling; tumor detection.
Citation
인터넷정보학회논문지, v.21, no.2, pp 19 - 26
Pages
8
Journal Title
인터넷정보학회논문지
Volume
21
Number
2
Start Page
19
End Page
26
URI
https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/25413
DOI
10.7472/jksii.2020.21.2.19
ISSN
1598-0170
Abstract
This paper analyses the feasibility of using implantable antennas to detect and monitor tumors. We analyze this setting according to the wireless propagation loss and signal fading produced by human bodies and their environment in an indoor scenario. The study is based on the ITU-R propagation recommendations and prediction models for the planning of indoor radio communication systems and radio local area networks in the frequency range of 300 MHz to 100 GHz. We conduct primary estimations on 915 MHz and 2.4 GHz operating frequencies. The path loss presented in most short-range wireless implant devices does not take into account the human body as a channel itself, which causes additional losses to wireless designs. In this paper, we examine the propagation through the human body, including losses taken from bones, muscles, fat, and clothes, which results in a more accurate characterization and estimation of the channel. The results obtained from our simulation indicates a variation of the return loss of the spiral antenna when a tumor is located near the implant. This knowledge can be applied in medical detection, and monitoring of early tumors, by analyzing the electromagnetic field behavior of the implant. The tumor was modeled under CST Microwave Studio, using Wisconsin Diagnosis Breast Cancer Dataset. Features like the radius, texture, perimeter, area, and smoothness of the tumor are included along with their label data to determine whether the external shape has malignant or benign physiognomies. An explanation of the feasibility of the system deployment and technical recommendations to avoid interference is also described.
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