Classification of Microwave Scattering Data based on a Subspace Distance with Application to Detection of Bleeding Stroke
Paper in proceedings, 2009

This paper demonstrates the usefulness of a classifier based on a subspace distance for the detection of bleeding stroke based on microwave scattering measurements from an antenna array placed around the skull. This discriminating classifier is suitable for high dimensional data applications when the number of training data samples is less than the data dimension. The proposed classifier was tested on both clinical and experimental data to separate bleeding subjects from non-bleeding ones. A pseudo-inverse Mahalanobis distance based classifier and a classifier based on the Euclidean distance were used on clinical data for the purpose of comparison with the proposed classifier.

Author

Mohammad Ali Khorshidi

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Tomas McKelvey

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Mikael Persson

Bioelectromagnetics

Hana Dobsicek Trefna

Bioelectromagnetics

2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2009; Aruba; Netherlands; 13 December 2009 through 16 December 2009

301-304

Subject Categories

Signal Processing

DOI

10.1109/CAMSAP.2009.5413272

ISBN

978-142445180-7

More information

Created

10/8/2017