Detection of brain hemorrhage in white matter using analysis of radio channel characteristics

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Abstract

This paper presents a simulation-based study on detection of stroke/brain hemorrhage even in the white matter using radio channel characteristics analysis. The idea is to utilize the fact that blood has different dielectric properties than brain’s white and grey matters and, thus, additional blood areas inside the brain change radio channel characteristics between the transmitter and receiver antennas located on the opposite sides of the head. The antennas should be strongly directive and designed to work attached to the body surface so that hemorrhages even in the white matter could be detected. The study is conducted using the electromagnetic simulation software CST and two different simulation models: a spherical tissue layer model and an anatomical voxel model. The antennas used in this study are bio-matched mini-horn antennas designed for implant communications at 1–4 GHz frequency range. Different sizes of the blood areas are evaluated. This initial study shows how even small sizes of hemorrhage can change radio channel even as the hemorrhage is located in the middle of the brain, in the white matter. The path loss difference is 0.5–10 dB between the hemorrhage and reference cases depending on the size and location of the hemorrhage. A practical solution of this hemorrhage detection technique could be a portable helmet type of structure having several small sized antennas around the internal part of the helmet. Such a helmet would be easy to use e.g. in ambulance, which would enable early detection of hemorrhage in its early phase and, hence, improve prospects of the cure significantly.

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Särestöniemi, M., Pomalaza-Raez, C., Hakala, J., Myllymäki, S., Kilpijärvi, J., Iinatti, J., … Myllylä, T. (2020). Detection of brain hemorrhage in white matter using analysis of radio channel characteristics. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 330, pp. 34–45). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-64991-3_3

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