Data-driven modeling of impedance biosensors: a subspace approach

A data-driven scheme for modeling electrical impedance in biosensors is presented by a subspace method working with the singular value decomposition of structured voltage and current data. Contrary to the classical electrical impedance spectroscopy (EIS) methods, our scheme uses simple instrumentation, works in time-domain, provides fast results, and does not require semi-empirical assumptions to retrieve structured models from data. We show how data-driven models exhibit a close relationship with lumped-element circuits, encoding dielectric and conductive properties detected by the sensor in the range from 10 kHz up to 10 MHz. Performance results are shown for calibration networks and two case studies: (i) a buffer solution, and (ii) a biological cell suspension. Finally, the viability of the scheme is discussed when compared with the classical EIS method.