I. Tabet. Development and Quantitative Analysis of Automatic Electrical Impedance Tomography Signal Component Detection and Separation Algorithms. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Bachelorarbeit. 2019
Abstract:
Pulmonary diseases are responsible for over 3 million deaths per year worldwide and more people are currently suffering from underdiagnosed pulmonary diseases. A proper diagnosis of pulmonary diseases requires an adequate respiratory monitoring. With the electrical impedance tomography (EIT) it is already possible to visualize the distribution of air inside the lungs and even the estimation of pulmonary perfusion by the use of the indicator dilution theory provides promising results. For perfusion estimation, a bolus of saline solution is injected to monitor the impedance changes caused by the higher conductivity of the injected indicator. However, this has only been shown for measurements in absence of respiration so far which requires a potentially strenuous ventilatory hold. This thesis deals with the detection of bolus signals during mechanical ventilation and their separation from respiration, which might further allow for a clinically more accepted method. The bolus detection was realized using a matched filter (MF) approach and evaluated on measured data with regard to sensitivity and specificity. Synthetic EIT signals were generated and used to analyze a separation based on frequency filtering. The separation quality was investigated using multiple error and similarity measures to compare original and separated signals. The automatic bolus detection algorithm provided satisfying results with a sensitivity over 96 % and specificity of 92 % for bolus injections with 5 % NaCl concentration. Further- more, the separation of those signals yielded correlation coefficients greater 0.94. However, morphological signal parameter differed between bolus signals before and after separation, which could lead to a misinterpretation of EIT indicator perfusion measurements.