Publication | Closed Access
<scp>R</scp>eal‐time brain stroke detection through a learning‐by‐examples technique—<scp>A</scp>n experimental assessment
47
Citations
8
References
2017
Year
EngineeringMachine LearningBraincomputer InterfaceCerebrovascular DiseaseBrain LesionBiomedical Signal AnalysisSupport Vector MachineImage AnalysisPattern RecognitionStrokeNeurologyNeurorehabilitationSvm PhasesNeurological MonitoringNeuroimagingCerebral Blood FlowBrain ImagingNeurological AssessmentBrain-computer InterfaceEeg Signal ProcessingNeuroscienceBrain ElectrophysiologyBrain StrokesBrain Stroke DetectionMedicine
Abstract 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.
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