Abstract
Electrical impedance tomography (EIT) of the head has the potential to provide rapid characterization of brain stroke. This study builds on previous work by implementing a more anatomically complex head model, contrasting results of bleed and clot simulations, and by establishing the electrodes which dominate in voltage difference measurements. This work provides the basis for machine learning with clusters of small numbers of electrodes as unique features for stroke-type detection and differentiation.Clinical Relevance- This application of EIT can aid in early detection, classification, and localization of brain stroke, allowing for faster treatment.
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