Zhang Ge, Dai Meng, Yang Lin, Li Weichen, Li Haoting, Xu Canhua, Shi Xuetao, Dong Xiuzhen, Fu Feng
Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Biomed Eng Online. 2017 Jan 7;16(1):7. doi: 10.1186/s12938-016-0294-7.
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.
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.
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%.
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.
在临床环境中进行脑电阻抗断层成像（EIT）长期监测时，电极断开是常见现象。数据采集系统会遭受显著的数据丢失，从而导致图像重建失败。本研究的目的是：（1）检测断开的电极，以及（2）处理无效数据。
基于连接良好和断开的电极之间的测量差异，计算每个电极的加权相关系数。通过离散小波变换滤除异常系数来识别断开的电极。此外，利用先前有效的测量值建立灰色模型。电极断开后的无效帧用灰色模型估计的数据替代。所提出的方法在电阻体模上以及对八名临床患者进行了评估。
所提出的方法能够检测出1个或2个断开的电极，准确率为100%；检测出3个和4个断开的电极时，准确率分别为92%和84%。电极检测的时间成本在0.018秒以内。此外，所提出的方法能够补偿至少60个后续数据帧，并在0.4秒内恢复正常的图像重建，平均相对误差小于0.01%。
在本文中，我们提出了一种两步法来检测多个断开的电极，并补偿断开后的数据无效帧。与先前研究中提出的方法相比，我们的方法能够以更高的准确率检测出更多断开的电极。此外，我们的方法在测量故障期间提供估计值，直到医务人员重新连接电极。这项工作将提高动态脑EIT的临床实用性，并有助于其进一步推广。
