Towards a Patient-Specific Model of Lung Volume Using Absolute Electrical Impedance Tomography (aEIT)

Electrical Impedance Tomography (EIT), and in particular its application to pulmonary measurement, has been the subject of intensive research since its development in the early 1980s by Barber and Brown. One of the relatively recent advances in EIT is the development of an absolute EIT system (aEIT) which can estimate absolute values of lung resistivity and associated lung volumes. In this paper we present a new approach based on Computational Intelligence (CI) modelling to model the ‘Resistivity - Lung Volume’ relationship that will allow more accurate lung volume estimations using data from eight (8) healthy volunteers measured simultaneously via the Sheffield aEIT system and a Spirometer. The developed models show an improved accuracy in the prediction of lung volumes, as compared with the original Sheffield aEIT system. However the inter-individual differences observed in the subject-specific modelling behaviour of the ‘Resistivity-Lung Volume’ curves suggest that a model extension is needed, whereby the modelling structure auto-calibrates to account for subject (or patient-specific) inter-parameter variability