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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.