Managing erroneous measurements of dynamic brain electrical impedance tomography after reconnection of faulty electrodes

Abstract

Abstract Objective: Electrode detachment may occur during dynamic brain electrical impedance tomography (EIT) measurements. After the faulty electrodes have been reset, EIT can restore to steady monitoring but the corrupted data, which will challenge interpretation of the results, are notoriously difficult to recover. Approach: Here, a piecewise processing method (PPM) is introduced to manage the erroneous EIT data after reattachment of faulty electrodes. In the PPM, we define the three phases before, during and after reconnection of the faulty electrode as PI, PII and PIII, respectively. Using this definition, an empirical mode decomposition-based interpolation method is introduced to compensate the corrupted data in PII, using the valid measurements in PI and PIII. Then, the compensated data in PII are spliced at the end of PI. Thus, there will be a surge at the junction of PII and PIII due to the changes in contact state of the repositioned electrodes. Finally, to ensure all the EIT data are obtained under constant electrode settings, we calculate the above changes and eliminate them from the data after PII. To verify the performance of the PPM, experiments based on head models, with anatomical structures and with human subjects were conducted. Metrics including permutation entropy (PE) and image correlation (IC) were proposed to measure the stability of the signal and the quality of the reconstructed EIT images, respectively. Main results: The results demonstrated that the PE of the processed data was reduced to 0.25 and the IC improved to 0.78. Significance: Without iterative calculations the PPM could efficiently manage the erroneous EIT data after reattachment of the faulty electrodes.