Fast detection and data compensation for electrodes disconnection in long-term monitoring of dynamic brain electrical impedance tomography. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Zhang, Ge; Dai, Meng; Yang, Lin; Li, Weichen; Li, Haoting; Xu, Canhua; Shi, Xuetao; Dong, Xiuzhen; Fu, Feng
• Is Part Of: Biomedical engineering online. Volume 16:Issue 1(2017)
• Page Start: 1
• Page End: 23
• Publication Date: 2017-12
• Subjects: Brain electrical impedance tomography -- Electrode disconnection -- Weighted-correlation coefficient -- Wavelet transform -- Grey model; Biomedical engineering -- Periodicals; 610.2805
• DOI: 10.1186/s12938-016-0294-7
• Languages: English
• ISSNs: 1475-925X

Abstract

Abstract Background 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. Methods Weighted 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. Results The 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%. Conclusions In 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.