FDTD based simulation study of a classification based hemorrhagic stroke detector
Paper in proceedings, 2018

The possibility to detect intracranial bleedings caused by stroke or head trauma in a prehospital setting would be a major breakthrough in the strive to deliver the best possible care for these patients. Our research is focused on developing a microwave based diagnostic system for prehospital use, which is capable of detecting intracranial bleedings. This paper contains a numerical simulation study to investigate the detection capability of a machine learning algorithm and its performance for differentiating hemorrhagic stroke from patients with no bleeding. The goal is to assess the performance of the detection algorithm as a function of the number of patients included in the training phase. The results show that this approach is feasible, but that it requires one thousand patients for training the algorithm, in order to reach a detection rate with AUC values approaching 0.9.

Stroke

Classification

FDTD simulation

Microwave diagnostics

Machine learning

Author

Andreas Fhager

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Biomedical Electromagnetics

Sahlgrenska University Hospital

Stefan Candefjord

Sahlgrenska University Hospital

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Biomedical Electromagnetics

Mikael Persson

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik

Sahlgrenska University Hospital

IET Conference Publications

Vol. 2018 CP741

12th European Conference on Antennas and Propagation, EuCAP 2018
London, United Kingdom,

Subject Categories

Medical Laboratory and Measurements Technologies

Biomedical Laboratory Science/Technology

Radiology, Nuclear Medicine and Medical Imaging

More information

Latest update

1/21/2019