Electrical impedance tomography (EIT) is a medical imaging technique based on the injection of a current or voltage pattern through electrodes on the skin of the patient, and on the reconstruction of the internal conductivity distribution from the voltages collected by the electrodes. Compared to other imaging techniques, EIT shows significant advantages: it does not use ionizing radiation, is non-invasive and is characterized by high temporal resolution. Moreover, its low cost and high portability make it suitable for real-time, bedside monitoring. However, EIT is also characterized by some technical limitations that cause poor spatial resolution. The possibility to design wearable devices based on EIT has recently given a boost to this technology. In this paper we reviewed EIT physical principles, hardware design and major clinical applications, from the classical to a wearable setup. A wireless and wearable EIT system seems a promising frontier of this technology, as it can both facilitate making clinical measurements and open novel scenarios to EIT systems, such as home monitoring.

Oscillometry is increasingly used for diagnosing respiratory conditions by measuring the respiratory system mechanical impedance. To minimize misdiagnosis, the accuracy of the measures and the factors that may impair it need to be fully investigated. Mathematical models are a useful tool for simulating measurements in various known conditions, providing information to evaluate the effect of different devices or processing methods on measurement accuracy. Previous studies demonstrated that signal processing methods impact the results of oscillometry measurements. However, the impact of the oscillometry device input resistance has not been analyzed. This work presents a mathematical model of the oscillometry device coupled with the patient's respiratory system to evaluate the impact of the device input resistance on measurement accuracy in infants. In this study, the model is used to generate synthetic oscillometry data using real breathing patterns and varying device resistance. We processed the simulated data to calculate respiratory system resistance and reactance using a least-mean-squares method. The results were compared with the resistance and reactance values used in the model. The error averaged over different real breathing patterns was proven to be influenced by the increase in the device resistance, both in linear and quadratic terms. When considering real newborns' breathing patterns, to maintain a mean and maximum error on resistance and reactance below 10^%- as per the published technical standards for in-vitro tests - we found that the device resistance must be lower than or equal to 2 $cmH_{2}O\cdot s/\mathrm{L}$. The developed model can be useful in the development of device requirements for accurate measurements.