Stroke Localization Using Multiple Ridge Regression Predictors Based on Electromagnetic Signals

Shang Gao, Guohun Zhu (), Alina Bialkowski and Xujuan Zhou
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Shang Gao: School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Brisbane, QLD 4072, Australia
Guohun Zhu: School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Brisbane, QLD 4072, Australia
Alina Bialkowski: School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Brisbane, QLD 4072, Australia
Xujuan Zhou: School of Business, University of Southern Queensland, Springfield, Brisbane, QLD 4300, Australia

Mathematics, 2023, vol. 11, issue 2, 1-9

Abstract: Localizing stroke may be critical for elucidating underlying pathophysiology. This study proposes a ridge regression–meanshift (RRMS) framework using electromagnetic signals obtained from 16 antennas placed around the anthropomorphic head phantom. A total of 608 intracranial haemorrhage (ICH) and ischemic (IS) signals are collected and evaluated for RRMS, where each type of signal contains two different diameters of stroke phantoms. Subsequently, multiple ridge regression predictors then give the target distances from the antennas and mean shift is used to cluster the predicted stroke location based on these distances. The test results show that the training time and economic cost are significantly reduced as the average prediction time only takes 0.61 s to achieve an accurate result (average position error = 0.74 cm) using a conventional laptop. It has great potential to be used as an auxiliary standard medical method, or rapid diagnosis of stroke patients in underdeveloped areas, due to its rapidity, good deployability, and low hardware cost.

Keywords: antenna array; electromagnetic signal; machine learning; mean-shift; ridge regression; stroke; vector network analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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