Monitoring the Effect of Contact Pressure on Bioimpedance Measurements

Ex vivo electrical bioimpedance measurements and Cole modelling on the porcine colon and rectum

Different pathological changes in the large intestine wall, associated with the development of different chronic diseases, including colorectal cancer, could be reflected in electrical bioimpedance readings. Thickness and composition of the mucus bilayer covering it in the luminal side, abundance of bacteria of the intestinal microbiota, the permeability of the epithelium and inflammation are some of these. However, scientific literature on electrical passive properties of the large intestine is scarce. In this study, complex impedance measurements at 8 frequencies were carried out on 6 specimens of porcine colorectal tissue, within half ab hour post-mortem, obtained from a local abattoir. For 5 different distances, measured proximally from the border of the anus, 3 readings were taken at 3 different points with a tetrapolar probe. The results show 2 different dielectric dispersions in the α and β regions and it seems that there is a relationship between the values of resistivities and the thickness of the wall. Also, parameter values both for the Cole and the geometrical models are given. Another set of electrical bioimpedance readings was carried out in order to assess the effect of the mucus layer on electrical properties of the tissue. It seems that these layers are related to the low frequency dispersion. Finally, electrical passive properties of porcine colorectal tissue, reported in this work, give reference values and behaviour patterns that could be applied for further research in human medicine, based on bioimpedance measurements.

Towards Real-time Multiplexed Bioimpedance Tumour-Tissue Margin Analysis

Bioimpedance varies with physical tissue characteristics. As such it can be used for real-time tissue discrimination. This has led to its application as a surgical mapping tool to differentiate between healthy and abnormal tissue intraoperatively during tumour resection. Here, we build on previous work implementing a probe-based tetrapolar bioimpedance systems demonstrator, now extracting additional information for margin analysis with imperfect bioimpedance visibility. Through finite element analysis, we show preliminary findings using a single measurement with a multiplexed tetrapolar bioimpedance probe for identifying tissue boundaries, applied to porcine tissue as a surrogate for a tumour-tissue interface.

Possibilities in the Application of Machine Learning on Bioimpedance Time-series

The relation between a biological process and the changes in passive electrical properties of the tissue is often non-linear, in which developing prediction models based on bioimpedance spectra is not trivial. Relevant information on tissue status may also lie in characteristic developments in the bioimpedance spectra over time, often neglected by conventional methods. The aim of this study was to explore possibilities in machine learning methods for time series of bioimpedance spectra, where we used organ ischemia as an example. Based on published data on the development of the bioimpedance spectrum during liver ischemia, a simulation model was made and used to generate sets of synthetic data with different levels of organ-to-organ variation, measurement noise and drift. Three types of artificial neural networks were employed in learning to predict the ischemic duration, based on the simulated datasets. The simulated prediction performance was very dependent on the amount of training examples, the organ-to-organ variation and the selection of input variables from the bioimpedance spectrum. The performance was also affected by noise and drift in the measurement, but a recurrent neural network with long short-term memory units could obtain good predictions even on noisy and drifting measurements. This approach may be relevant for further exploration on several applications of bioimpedance having the purpose of predicting a biological state based on spectra measured over time.