A multidimensional signal processing approach for classification of microwave measurements with application to stroke type diagnosis

A multidimensional signal processing method is described for detection of bleeding stroke based on microwave measurements from an antenna array placed around the head of the patient. The method is data driven and the algorithm uses samples from a healthy control group to calculate the feature used for classification. The feature is derived using a tensor approach and the higher order singular value decomposition is a key component. A leave-one-out validation method is used to evaluate the properties of the method using clinical data.

[1]  L. Tucker,et al.  Some mathematical notes on three-mode factor analysis , 1966, Psychometrika.

[2]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[3]  Joos Vandewalle,et al.  A Multilinear Singular Value Decomposition , 2000, SIAM J. Matrix Anal. Appl..

[4]  Berkant Savas,et al.  Handwritten digit classification using higher order singular value decomposition , 2007, Pattern Recognit..

[5]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[6]  J. Grotta,et al.  Guidelines for Thrombolytic Therapy for Acute Stroke: a Supplement to the Guidelines for the Management of Patients with Acute Ischemic Stroke. A statement for healthcare professionals from a Special Writing Group of the Stroke Council, American Heart Association. , 1996, Stroke.

[7]  H. Ellis.  stroke , 1997, The Lancet.

[8]  Andreas Fhager,et al.  Stroke detection using a broadband microwave antenna system , 2010, EuCAP 2010.

[9]  L. Lathauwer,et al.  Signal Processing based on Multilinear Algebra , 1997 .

[10]  Mikael Persson,et al.  Classification of microwave scattering data based on a subspace distance with application to detection of bleeding stroke , 2009, 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[11]  A. Fhager,et al.  3D Image Reconstruction in Microwave Tomography Using an Efficient FDTD Model , 2009, IEEE Antennas and Wireless Propagation Letters.

[12]  Serguei Semenov,et al.  Microwave tomography for functional imaging of extremity soft tissues: feasibility assessment , 2007, Physics in medicine and biology.

[13]  J. Saver.  Time Is Brain—Quantified , 2006, Stroke.

[14]  J Perl,et al.  Thrombolytic therapy for acute ischemic stroke. , 2001, Techniques in vascular and interventional radiology.