Abstract
BackgroundElectrode disconnection is a common occurrence during long-term monitoring of brain electrical impedance tomography (EIT) in clinical settings. The data acquisition system suffers remarkable data loss which results in image reconstruction failure. The aim of this study was to: (1) detect disconnected electrodes and (2) account for invalid data.MethodsWeighted correlation coefficient for each electrode was calculated based on the measurement differences between well-connected and disconnected electrodes. Disconnected electrodes were identified by filtering out abnormal coefficients with discrete wavelet transforms. Further, previously valid measurements were utilized to establish grey model. The invalid frames after electrode disconnection were substituted with the data estimated by grey model. The proposed approach was evaluated on resistor phantom and with eight patients in clinical settings.ResultsThe proposed method was able to detect 1 or 2 disconnected electrodes with an accuracy of 100%; to detect 3 and 4 disconnected electrodes with accuracy of 92 and 84% respectively. The time cost of electrode detection was within 0.018 s. Further, the proposed method was capable to compensate at least 60 subsequent frames of data and restore the normal image reconstruction within 0.4 s and with a mean relative error smaller than 0.01%.ConclusionsIn this paper, we proposed a two-step approach to detect multiple disconnected electrodes and to compensate the invalid frames of data after disconnection. Our method is capable of detecting more disconnected electrodes with higher accuracy compared to methods proposed in previous studies. Further, our method provides estimations during the faulty measurement period until the medical staff reconnects the electrodes. This work would improve the clinical practicability of dynamic brain EIT and contribute to its further promotion.
Highlights
Electrode disconnection is a common occurrence during long-term monitoring of brain electrical impedance tomography (EIT) in clinical settings
We develop a real-time detection for multiple disconnected electrodes to alert medical staff and to help to fix the disconnected electrodes as soon as possible
The methodology developed to manage the disconnection of electrodes is described in “Analyzing the influence of electrode disconnection on measurements”, “Calculation of electrode variation coefficient”, “Detection of disconnected electrodes based on wavelet decomposition” and “Compensation algorithm based on grey model method” sections
Summary
Electrode disconnection is a common occurrence during long-term monitoring of brain electrical impedance tomography (EIT) in clinical settings. The data acquisition system suffers remarkable data loss which results in image reconstruction failure. The aim of this study was to: (1) detect disconnected electrodes and (2) account for invalid data. Dynamic brain electrical impedance tomography (EIT) reconstructs the changes in intracranial conductivities at two different instants by injecting safe currents and. Well-connected electrodes are a prerequisite for normal data acquisition and image reconstruction. Dynamic brain EIT monitoring is a long-term process. The disconnection affects the quality of acquired data, which gives rise to reconstruction failure. It is essential to investigate the case of disconnected electrodes in clinical experiments for improving the applicability of long-term monitoring
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