z-logo
Premium
R eal‐time brain stroke detection through a learning‐by‐examples technique— A n experimental assessment
Author(s) -
Salucci Marco,
Vrba Jan,
Merunka Ilja,
Massa Andrea
Publication year - 2017
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.30821
Subject(s) - support vector machine , computer science , artificial intelligence , machine learning , function (biology) , brain function , pattern recognition (psychology) , neuroscience , psychology , evolutionary biology , biology
The real‐time detection of brain strokes is addressed within the Learning‐by‐Examples (LBE) framework. Starting from scattering measurements at microwave regime, a support vector machine ( SVM ) is exploited to build a robust decision function able to infer in real‐time whether a stroke is present or not in the patient head. The proposed approach is validated in a laboratory‐controlled environment by considering experimental measurements for both training and testing SVM phases. The obtained results prove that a very high detection accuracy can be yielded even though using a limited amount of training data.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom