Brain haemorrhage detection through SVM classification of impedance measurements

Abstract

Machine Learning is becoming increasingly important in interpreting biological signals. In this work, we examine the potential for classification in brain haemorrhage detection. Numerical head and brain models with and without haemorrhagic lesions are designed. Impedance measurements from an electrode array positioned on the exterior of the head are used to train and test linear support vector machine (SVM) classifiers. The results show that this emerging measurement technique may have promise for detection and diagnosis of brain haemorrhage when coupled with such classifiers.