Fast detection and data compensation for electrodes disconnection in longterm monitoring of dynamic brain electrical impedance tomography
 Ge Zhang†^{1},
 Meng Dai†^{1},
 Lin Yang^{1},
 Weichen Li^{1},
 Haoting Li^{1},
 Canhua Xu^{1},
 Xuetao Shi^{1},
 Xiuzhen Dong^{1}Email author and
 Feng Fu^{1}Email author
DOI: 10.1186/s1293801602947
© The Author(s) 2017
Received: 28 August 2016
Accepted: 4 December 2016
Published: 7 January 2017
Abstract
Background
Electrode disconnection is a common occurrence during longterm 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 wellconnected 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 twostep 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.
Keywords
Brain electrical impedance tomography Electrode disconnection Weightedcorrelation coefficient Wavelet transform Grey modelBackground
Dynamic brain electrical impedance tomography (EIT) reconstructs the changes in intracranial conductivities at two different instants by injecting safe currents and measuring boundary voltages through 16 or more surface electrodes [1, 2]. Therefore, wellconnected electrodes are a prerequisite for normal data acquisition and image reconstruction. However, dynamic brain EIT monitoring is a longterm process. Electrode disconnection is a common occurrence because of several factors such as patient body movement, conscious or unconscious head rotation, and operations by medical staffs [3, 4]. The disconnection affects the quality of acquired data, which gives rise to reconstruction failure. Therefore, it is essential to investigate the case of disconnected electrodes in clinical experiments for improving the applicability of longterm monitoring.
In contrast to other conventional longterm physiological parameter monitors such as electrocardiogram monitors, the electrodes of brain EIT systems are laid under bandages around the transverse plane of the head. Therefore, it is difficult to visually discover the disconnected electrodes. Disconnected electrodes can be detected by improving hardware of EIT systems. However, the hundreds of measuring channels with many possible electrode combinations make redesigning the data acquisition system troublesome. Moreover, such improvements would not help to compensate for invalid data produced by disconnected electrodes. Therefore, a fast and convenient method is needed to discover disconnected electrodes and to compensate for the invalid data using a specific algorithm based on the characteristics of measurement.
Some similar studies have been performed in lung EIT. Adler proposed a methodology that calculated the image with remaining good data by modifying the noise covariance matrix in maximum a priori (MAP) reconstruction algorithm [3]. However, this method requires priori information of the disconnected electrode. Asfaw and Adler realized automatic detection of detached electrodes based on comparisons between measured voltages and simulated voltages, but it is not applicable in realtime [5]. Hartinger et al. [4] presented a detection method for faulty electrodes’ management based on reciprocal principle. This method’s detectability for multiple disconnected electrodes still needs improvement and the application requires extra reciprocity measurement. Nevertheless, the dynamic lung EIT is different from dynamic brain EIT because the imaging interval between the reference data and current data is shorter [6, 7]. Besides, there are other studies of electrode error detection in multisensor devices [8–10]. However, their application conditions are not suitable for our brain EIT case. Recent studies of EIT electrodes have primarily concentrated on the impact of electrode–skin contact impedance on EIT image quality, which is a different problem from disconnection [11–14].
In this study, we develop a realtime detection for multiple disconnected electrodes to alert medical staff and to help to fix the disconnected electrodes as soon as possible. And compensation for invalid data is proposed to restore the image reconstruction, which is necessary for medical staff to gain approximate monitoring results while the data acquisition electrodes are disconnected. The novelty of our proposed approach is as follows. Without modifying the data acquisition protocol or the reconstruction algorithm as proposed in previous studies, we presented a measureddatabased approach to deal with the electrode disconnection by two steps. For disconnected electrode detection, we utilized the measured voltages and correlation coefficients to calculate weighted correlated coefficient for each electrode, and distinguished the EVC values corresponding to disconnected electrodes by wavelet transform. Besides, we employed EVC calculation in a circular fashion to unify the EVC calculation environments and simplify complicated scenarios with different number and location of disconnected electrodes into limited cases. In the data compensation, we utilize grey model prediction established by previous good data to replace the lost frames of date.
Methods
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 . In “Data acquisition procedure” section, we describe the details about data acquisition and experimental operations.
Analyzing the influence of electrode disconnection on measurements
In this section, we illustrate the difference between measurements with disconnections and measurements without disconnections via a theoretical analysis and phantom tests.
There are two primary data collection strategies for EIT data acquisition, namely pairdriven and multipledriven electrode systems. The applied potential tomography (APT) and adaptive current tomography (ACT) systems are corresponding implementations [15]. The typical APT system was developed by Sheffield University [16]. This system attaches 16 electrodes at equal distances around the body surface. Successively applied current is injected through a pair of electrodes, and the voltages between other adjacent noncurrentcarrying electrodes are measured. A frame of data is collected after the procedure is repeated for each adjacent pair. In dynamic imaging, the APT system uses one frame of data as a reference and another frame of measurement data as the current data. In the reconstruction algorithm, if there is any impedance change that leads a variation in the boundary voltage, the internal impedance change will be displayed in the image. The ACT system applies current to all electrodes and simultaneously measures the voltage on all electrodes [17]. For an ACT system with L electrodes, there are L(L − 1)/2 independent measurements, since at most L − 1 independent currents can be applied and the currenttovoltage operator is selfjoint [15, 18].
Calculation of electrode variation coefficient
In this section, we propose a metric termed the electrode variation coefficient (EVC), which is obtained by calculating the weighted correlation coefficients for each electrode, and we determine disconnected electrodes by distinguishing abnormal EVC values.
The details of each step are given as follows:
Detection of disconnected electrodes based on wavelet decomposition
Based on the EVC calculation method and the theoretical analysis above, we conclude that the EVC values of connected electrodes are consistent and tend to a trivial value, while the EVC values of disconnected electrodes tend to relatively high values. If the EVC values are sorted in ascending order, the EVC sequence of the connected electrodes is a relatively smooth and slowly varying parameter. Thus, the EVC sequence of disconnected electrodes reveals mutation points of high amplitude. In the signal analysis, such slowly changing signals are considered low frequency while dramatically changing signals are considered high frequency. Wavelet transformation is an effective tool for detecting the discontinuity points [8, 27]. The detailed decomposition coefficient at discontinuity points is rather high while the other coefficients spread around zero [28]. Therefore, the reconstructed detail coefficient would have higher amplitude at the discontinuity point, which is also the first point corresponding to disconnected EVCs. Therefore, we can localize the group of EVCs corresponding to disconnected electrodes by identifying the frequency discontinuity point in the ascending EVC sequence [27]. In our study, we used db4 wavelet function to perform the wavelet transform and to localize the discontinuity by detecting the minimum value.
If there are no disconnected electrodes, x _{ EVC } will be consistent. If there are disconnected electrodes, there will be mutation points in x _{ EVC } and minimum value in \( \hat{x}_{EVC} \). As a result, we chose \( \hat{x}_{EVC} (\hbox{min} ) \) as the criterion of selection, electrodes with indices higher than the minimum are disconnected electrodes.
Compensation algorithm based on grey model method
Dynamic brain EIT is a continuous process of data acquisition and monitoring, therefore, under normal circumstances the measured data has certain continuity [31–33]. For a particular time instants, the data can be regarded as the continuation of the previous period of data [34, 35]. The predata contain the potential changes of the monitoring object. Therefore, the data prediction method can be used to estimate the original data based on mathematical model calculated by using prior reliable measurements.
The grey systems theory was proposed by Deng in 1982 [36]. Grey prediction is an estimation of a grey system. Grey predication makes scientific, quantitative forecasts about the future output of a system by generating and extracting the useful information from a small number of samples and partially known information, which has a good application in the engineering field [37, 38]. The single variable first order grey model, which is abbreviated as GM(1,1), is the main and basic model of grey prediction. In this study, GM(1,1) is used to compensate for the invalid frames of data.
There are 4 main steps in this part: Generating the accumulation sequence, generating the reverse accumulation, establishing the grey model and data calculation. The detailed procedures are as follows:
(i) Generate the accumulation sequence. For one given measurement channel, the original data is expressed as X ^{(0)} = {x ^{(0)}(1), x ^{(0)}(2), …, x ^{(0)}(n)}, where n is the sample size of data. Based on our experimental experience, we chose n = 30. The firstorder accumulative generation converts X ^{(0)} to X ^{(1)} = {x ^{(1)}(1), x ^{(1)}(2), …, x ^{(1)}(n)}, where x ^{(1)}(k) = ∑ _{ i=1} ^{ k } x ^{(0)}(i), k = 1, 2, …, n. Then, the adjacent neighbor mean sequence is computed as Z ^{(1)} = {z ^{(1)}(2), z ^{(1)}(3), …, z ^{(1)}(n)}, where \( z^{(1)} (k) = \frac{{x^{(1)} (k  1) + x^{(1)} (k)}}{2}\;k = 2,3, \ldots ,n. \)
There are 192 valid data channels in the EIT system. We need to repeat the above 4 steps 192 times to obtain a complete frame. Based on our experience in the trials, here n was set to 60, which indicates that 60 continuous frames are needed for the calculation.
Data acquisition procedure
To verify the effectiveness of the proposed approach, experiments were performed on a resistor phantom and clinical patients. EIT data were measured in real time using an EIT system (FMMUEIT5). This system consists of 16 electrodes. The working frequency of the system ranges from 1 to 190 kHz, the current from 500 to 1250 μA with a measuring accuracy of ±0.01%. The commonmode rejection ratio is over 80 dB. We have carried out a series of experiments using this system and demonstrated is a reliable data acquisition system [34, 35]. A more detailed description of the EIT system is presented in previous studies [19, 20]. The reconstruction algorithm is damped least square method using finite element models [33]. In this study, 1 mA and 50 kHz altering current and 1 frame per second data acquisition speed was used. All calculation was implemented on a Pentium G630 computer.
The clinical data were acquired at Xijing hospital, Fourth Military Medical University, Xi’an, China. They were approved by the Fourth Military Medical University Ethics Committee on Human Research and informed written consent was obtained from the patients’ relatives. In electrode detection scenario, six patients (five males and one female) were included. All patients were conscious and lay on the sickbed. Before the monitoring, sixteen copper cup electrodes were rigorously sterilized and placed with the conductive gel (Ten 20 conductive paste, Weaver and Company, Aurora, USA) on the circumference of the head. The setup of disconnected electrodes was the same as in the phantom experiments. We simulated disconnection by detaching the electrodes from underneath of bandage around the head or connecting a resistor in series with the wire. In compensation scenario, data collected from the patients (two males) who received treatment of twist drill drainage in department of neurosurgery of Xijing hospital were analyzed retrospectively [34, 35]. The finite element model was obtained by segmenting the patient CT images into three parts (scalp, skull, parenchyma), which were further discretizing into 851 triangle elements. In both phantom and clinical trials, we selected a period of data to test the compensation method without setting disconnected electrodes.
Results
Detection of disconnected electrodes
Performance evaluation of the detection algorithm in clinical condition
Number of disconnected electrodes  Number of clinical datasets tested  Number of correctly detected disconnected electrodes  Percentage of cases 

0  12  0  100 
1  12  1  100 
2  12  2  100 
3  12  2  8 
3  92  
4  12  2  8 
3  8  
4  84  
5  12  2  25 
3  33  
4  25  
5  17 
Compensation for invalid frames of data
All parameters for comparison between the actual measurements and prediction
Development factor  MRE  Model level  MPRE (1st frame)  MPRE (60th frame)  MPCC  

Phantom without target  ≤3.73e−04  ≤0.0034  Qualified  ≤3.2834e−06  ≤3.2834e−06  0.8865 
Phantom with target  ≤4.67e−04  ≤0.005  Qualified  ≤1.7359e−05  ≤1.7698e−05  0.8543 
Clinical trial twist drill drainage  ≤5.21e−04  ≤0.0043  Qualified  ≤2.2145e−06  ≤6.5038e−06  0.7763 
Evaluation of time cost
Discussion
This paper analyzed the problem of electrode disconnection during brain EIT monitoring in clinical environments, and we proposed a twostep approach to detect disconnected electrodes and to compensate for the invalid data. The experiments with data from the resistor phantom and patients proved an effective approach for managing such disconnections.
Multiple disconnected electrodes detection
With the detection method, we can locate multiple disconnected electrodes with high accuracy to help medical staff to fix the disconnected electrodes and minimize the data loss as much as possible.
Different from previous studies, we defined the EVC values calculated from the weightedcorrelation coefficients to evaluate the connection of electrodes and singled out the disconnected electrodes via DWT. In previous studies, the detection approaches were either based on the inverse and forward problem calculation or reciprocal principle theory. In Asfaw‘s method, detached electrodes were automatically detected through repeating forward and inverse calculations. He proposed that a set of ‘good’ electrodes could produce measurements consistent with each other and such consistency would be terminated if ‘bad’ electrodes were contained in the set, and thus erroneous electrodes could be excluded by verifying the consistency of the measured data with the electrode sets. However, the detection method required n × n (where n is the electrode number) calculations of the forward problem and inverse problem and thus was not suitable for realtime detection. Furthermore, the method was designed to reliably detect one detached electrode. In Hartinger’s method, faulty electrodes are confirmed by examining voltagecurrent reciprocal measurements. Although the detection method was reliable for its intended purpose, the sensibility was low in case of more than one faulty electrode. In addition, extra data were required if the data acquisition protocol of the EIT system cannot provide reciprocal measurements.
Comparison of the results of two previous detection methods and the method reported in this study
Methods  Reported computing time  Reported detection number  Notes 

Asfaw et al. [5]  About 4 s  Designed for 1  Scenarios for 2 and 4 disconnected electrodes were shown 
Hartinger et al. [4]  Realtime, accurate time is not reported  1 with 100% accuracy 2 with 96% accuracy 3 with 8% accuracy  
The presented method  within 0.018 s  1 or 2 with 100% accuracy 3 with 92% accuracy 4 with 84% accuracy 
In addition, Ghanem implemented wavelet decomposition to detect lead failure of ECG electrodes [8]. Two parameters were extracted from the reconstructed approximation sequence and reconstructed detail sequence to build a no lead failure zone. If the point with the two parameter as X and Y coordinates falls in the lead failure zone, there is a lead failure in the acquired data which is processed by wavelet decomposition. Unfortunately, this implement is not suitable for brain EIT because EIT data has no significant spikes to extract and such detection might be hysteretic. Ross developed a system and method for correcting fault conditions in softfield tomography [9]. His projection detected fault excitation through mismatch response and compensated the valid data by a precalculated output. However, the successfulness relied on the redesign of hardware and therefore, not as practical as our method.
In the detection method, we use correlation coefficient to measure the similarity. This similarity can be represented by other statistical parameters, such as Euclid distance, Hausdorff distance, cosine distance and dynamic time warping [40–43]. These parameters are used to measure sequence similarity in many cases. However, the performance of the Euclid and Hausdorff methods may introduce issues in comparing between measurements from different frames because their calculations of absolute distance highly depending on the calculation baseline. The cosine distance focuses more on the direction change of the data sequence, not the shape of the curve. Dynamic time warping introduces more calculations and does not exhibit an apparent advantage over correlation coefficients [42]. To avoid potential problems and to cooperate with the following calculation, we selected the correlation coefficients. Besides, we utilize wavelet decomposition to filter out abnormal EVC values corresponding to disconnected electrodes. Here, we primarily take advantage of the time–frequency correspondence characteristic of the wavelet transform. Other time–frequency methods include the Hilbert transform, the short time Fourier transform and the quadratic time–frequency distribution [44–47]. These methods are also used to extract instantaneous characteristics of the signal. However, we need a very precise onetoone correspondence between the original EVC and the reconstructed EVC with the detail coefficients. The wavelet transform has the ability to provide high time resolution in high frequency to meet our requirements [48, 49]. Other methods do not offer such point to point accuracy.
During the experiments, to verify the effectiveness of the disconnected electrodes detection, we disconnected multiple adjacent electrodes to simulate various possible scenarios. With data acquired from each excitation, we only checked the two middle electrodes between the currentdriving electrode pair. According to Eq. 2, the amplitude of the measurement containing the middle electrode is usually less than other measurement on the same side, which makes it easier to preprocess the abnormal measurement. By locking the relative position of the target electrode, we simplify all possible scenarios of disconnection into four cases: ‘good’–‘bad’–‘good’, ‘bad’–‘bad’–‘good’, ‘bad’–‘good’–‘bad’, ‘bad’–‘bad’–‘bad’, while the middle electrode is the target to check. This design greatly decreases the logical complexity and location sensitivity. Experiments on the resistor phantom showed that all disconnected electrodes could be filtered out with 100% accuracy. However, due to noise and target differences between clinical and laboratory environments, we did not further evaluate the phantom tests after verification of the methodology principle. In clinical trials, before the monitoring was started, it should be ensured that all electrodes are wellconnected. The results from patients showed that the detection method was able to achieve very high accuracy if the number of disconnected electrodes is no more than four. However, disconnections would lead to interference in the measurements made by the data acquisition system in an ICU environment. Therefore, in several scenarios in which less than five electrodes were disconnected, the detection sensitivity was not able to reach 100%. Moreover, if there are five or more disconnected electrodes, the weighting part for calculating EVCs becomes very unstable and might affect the use of the minimum of wavelet reconstruction to determine abnormal EVC values, which would lead to missed disconnected electrodes. In the clinical experiments, more complicated explorations with more disconnected electrodes were not performed because other ‘good’ electrodes could be compromised when adjusting the disconnected electrodes if there are more than four disconnected electrodes. Therefore, removing the bandage to examine and fix all electrodes is a more reliable way to eliminate such disconnections. Otherwise, this detection principle of this method is not only suitable for EIT systems with oppositedrive adjacentmeasurement protocol, but also suitable for systems with pseudooppositedrive adjacentmeasurement protocol or other working protocols.
The electrode disconnections actually reflect malfunctions of data acquisition system. The most common numerical simulation method is not applied in this paper. The numerical simulation could display the voltage and current distribution of the imaging area, but for electrode disconnection, the affected data are not inexistent but missed in the transmission from the body to the EIT system through electrodes. The numerical simulation results are not related to the peripheral hardware. Therefore, we chose to show simulations on resistor phantom.
If an electrode is of incomplete contact, the contact impedance will increase compared with a wellconnected electrode. The contact impedance affects the EIT measurement in two ways. First, contact impedance affects the current distribution beneath the electrode inside the body [14]. Second, contact impedance causes the common mode voltage to yield a differential mode voltage at the amplifier input of EIT system [26]. The incomplete contact introduces interface into the voltage measurement through these two ways described above, which leads to artifacts in the image reconstruction. In circumstance of incomplete contact, current is still able to flow through the electrode. But in disconnection there is little drive current flow through the electrode into the body. So the voltage measurements from disconnected electrode become unstable, and the measurements acquired are different from the connected electrode in amplitude and curve shape, when the disconnected electrode acts as positive electrode or negative electrode.
The aim of detecting disconnected electrodes is to reduce data loss. In this study, the detection method presented in this study makes a judgment as to whether the electrode is disconnected or not. The method does not quantitatively reflect the contact status of the electrodes. Recent studies on EIT electrode primarily focus on electrode scenarios with incomplete contact, where there is still normal current injection, and the measurement error mainly comes from current distribution distortion caused by electrode–electrolyte layer [11, 13, 50]. Mamatjan et al. [51] proposed a method to quantitatively evaluate EIT data quality. The method provided an overall assessment of the whole dataset. In next step, we will evaluate each single electrode with detailed scenarios regarding where the current distribution is affected or the common mode error is included, because these issues are not clearly addressed at the present.
Compensation for invalid frames of data
In the compensation part, we employed the GM(1,1), which was established with normal measurements before disconnection, to predict the subsequent frames of data. The predicted data can be used to reconstruct images without disconnection artifacts. In previous studies, the compensation was based on reconstruction algorithm improvement [3, 4]. Adler and Hartinger both modified the measurement noise covariance matrix in the MAP reconstruction algorithm to compensate for invalid data. And they still required rest valid data to continue monitoring. Our prediction method does not need to recalculate the reconstruction matrix.
We made full use of the valid data before the disconnection occurred to establish GM(1,1) to predict the data to replace the invalid frames. Indeed this method could not utilize the residual valid data. However, in most circumstance of longterm monitoring, it is not possible to have intracranial pathological or physiological changes, which will lead to a dramatic change in the brain impedance in minutes. This means that the latter measurement could be considered an extension of the previous measurement. Therefore, it is reasonable to extract the features of existing valid data to predict the subsequent measurements in specific brain EIT monitoring. Our compensation method offers another thought that based on available frames of data rather than the reconstruction algorithm improvement to compensate the data loss.
The result of grey model compensation depends on the data used to establish the grey model. If the interval of two interruptions is shorter than the data frames that we need to establish grey model, the effect of compensation will be affected. If the contact is lost momentarily, the compensation method will start to compensate for invalid frames of data and the compensation procedure will not until all electrodes are connected. Whether the compensation is activated or inactivated depends on the disconnected electrode detection result. There is one scenario that the contact is lost immediately after the monitoring begins, the compensation algorithm could not compensate the lost data. Because we need a period of good data before disconnection happens to establish the grey model. However, the dynamic brain EIT monitoring is a longterm process, so the data loss at the beginning of monitoring has limited effect on the overall monitoring results.
One limitation of compensation algorithm is that it couldn’t utilize the residual valid data. But in most circumstance of long time monitoring, it is not possible to have intracranial pathological or physiological changes, which will lead to a dramatic change of the brain impedance in minutes [42, 43]. So although there are some limitations, the compensation method could meet our need.
After electrode disconnection happens, the reconstructed image will be invalid immediately. The grey model compensation is designed to offer the predicted image result until the clinical staff comes to solve the issue. Afterwards, a new reference frame will be established. The image result before disconnection cannot be shown in the new image. If we continue to monitor without reselecting reference frame, the position and contact status of readhering electrodes are changed compared with their initial conditions and artifacts will be introduced in monitoring image [3]. Therefore, in further research, we need to consider how to inherit the information of former image into new monitoring images by combining present techniques.
Conclusions
In clinical longterm dynamic brain EIT monitoring, electrode disconnections are a common occurrence and will lead to a failure of data acquisition. This paper offers a twostep solution to address the electrode detection. Our approach is able to detect more disconnected electrodes with a higher accuracy. The invalid frames of data were replaced by calculating a grey model with more stability. This proposed method is based on the features of available measurements rather than improving hardware or reconstruction algorithm in previous studies, which offers a novel way to deal with such problems.
Notes
Abbreviations
 EIT:

electrical impedance tomography
 MAP:

maximum a prior
 EVC:

electrode variation coefficient
 DWT:

discrete wavelet decomposition
 GM:

grey model
 MRE:

mean relative error
 MPRE:

mean posterior relative error
 MPCC:

mean posterior correlation coefficient
Declarations
Authors’ contributions
Conceived and designed the experiments: GZ MD LY WL. Performed the experiments: GZ MD. Analyzed the data: GZ MD. Contributed reagents/materials/analysis tools: HL CX XS XD FF. Wrote the paper: GZ MD. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
The datasets of the present study are available from the corresponding author on reasonable request.
Ethics approval and consent to participate
The study was approved by the Fourth Military Medicine University Ethics Committee on Human Research and informed written consent was obtained from those patients’ nearest relatives.
Funding
This work is supported by National Nature Science Foundation of China (Grant Number: 51477176), National Key Technology Support Program of China (Grant Number: 2011BAI08B13), Youth Program of National Nature Science Foundation of China (Grant Number: 31600799) and Shaanxi Province Science and Technology Program (Grant Number: 2016SF266).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
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