Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy
- Xiaolin Zhou^{1, 2}Email author,
- Hongxia Ding^{1, 2},
- Benjamin Ung^{3},
- Emma Pickwell-MacPherson^{3} and
- Yuanting Zhang^{1, 2, 3}Email author
https://doi.org/10.1186/1475-925X-13-18
© Zhou et al.; licensee BioMed Central Ltd. 2014
Received: 3 November 2013
Accepted: 22 January 2014
Published: 17 February 2014
Abstract
Background
Atrial fibrillation (AF) is the most common and debilitating abnormalities of the arrhythmias worldwide, with a major impact on morbidity and mortality. The detection of AF becomes crucial in preventing both acute and chronic cardiac rhythm disorders.
Objective
Our objective is to devise a method for real-time, automated detection of AF episodes in electrocardiograms (ECGs). This method utilizes RR intervals, and it involves several basic operations of nonlinear/linear integer filters, symbolic dynamics and the calculation of Shannon entropy. Using novel recursive algorithms, online analytical processing of this method can be achieved.
Results
Four publicly-accessible sets of clinical data (Long-Term AF, MIT-BIH AF, MIT-BIH Arrhythmia, and MIT-BIH Normal Sinus Rhythm Databases) were selected for investigation. The first database is used as a training set; in accordance with the receiver operating characteristic (ROC) curve, the best performance using this method was achieved at the discrimination threshold of 0.353: the sensitivity (Se), specificity (Sp), positive predictive value (PPV) and overall accuracy (ACC) were 96.72%, 95.07%, 96.61% and 96.05%, respectively. The other three databases are used as testing sets. Using the obtained threshold value (i.e., 0.353), for the second set, the obtained parameters were 96.89%, 98.25%, 97.62% and 97.67%, respectively; for the third database, these parameters were 97.33%, 90.78%, 55.29% and 91.46%, respectively; finally, for the fourth set, the Sp was 98.28%. The existing methods were also employed for comparison.
Conclusions
Overall, in contrast to the other available techniques, the test results indicate that the newly developed approach outperforms traditional methods using these databases under assessed various experimental situations, and suggest our technique could be of practical use for clinicians in the future.
Keywords
Background
Atrial fibrillation (AF) is recognized as the most common clinically encountered arrhythmia in adults [1], which affects approximately 0.4% of the general population. The prevalence of this tachyarrhythmia increases with age, with less than 1% affected in persons under the age of 60 years and in excess of 6% for those over the age of 80 years [2, 3]. Atrial fibrillation is associated with a high risk of stroke, heart disease (e.g., congestive cardiac failure), and cardiovascular mortality [1, 4]. There is also a close relationship between AF and obesity [5], obstructive sleep apnea [6], and long-term alcoholism [7], which reciprocally bear cumulative risks for promoting the development of AF [1]. The early identification of AF appears to be crucial for patients with cardiovascular disease, especially for stroke patients to whom the secondary stroke prevention is of primary importance.
Issues relating to clinical significance of rhythm classification and the impetus for improving the accuracy of atrial tachyarrhythmia estimation have motivated the development of innovative computerized AF detectors. Since the early 1980s, a series of sophisticated methods have been investigated to cope with the challenges of AF detection [8–25]. Most of which are based upon two main character traits of this type of arrhythmia shown in a surface electrocardiogram (ECG): (i) RR (R-wave peak to R-wave peak) interval irregularity (i.e., chaotic behavior of heart rate variability), and (ii) P-wave absence (PWA) or F-wave substitution (i.e., very low amplitude waveforms of odd morphologies) resulting from the abnormal rapid atrial activity (AA). Although P waves or cardiac AA can be an alternative clue in the detection of AF, the absence or presence of P waves are not readily identifiable as various types of high-intensity noise often coexist in ECGs, which may lead to a low degree of predictive accuracy. In addition, the relationship between AA in the surface ECG and the diverse mechanisms of AF has not yet been well delineated [3]. Due to the challenges in detecting AA in ECG measurements, detection techniques based on inferences from RR intervals are preferred to produce relatively robust outcomes [21–23, 25].
In this study, a reliable method for the fully automated detection of AF episodes from surface ECGs is proposed. This method comprises of a three-pass procedure. The initial pass, where a RR interval sequence is pre-processed with nonlinear and integer filters, which aims to generate low/high scale reference sequences. The second pass, which aims to obtain a symbolic sequence, where the information of the RR interval sequence is subsequently compressed by the symbolic dynamics with sequences obtained from the initial pass. Finally, Shannon entropy is used in the third pass, to calculate the entropy of the symbolic sequence and thereby discriminate whether or not AF is present in the current cardiac beat. Further methodological insight of present key points on the online analytical processing of measurements through the recursive realization with respect to beat-by-beat classification is discussed in the following sections. Ultimately, we quantitatively investigate the performance of our newly developed technique to that of currently state-of-the-art techniques with four widely used clinical databases under various experimental situations.
Methodology
Pre-processing of RR _{ n }series
A. Median filter
where the window is of a fixed width 2w+1. From the perspective of signal processing, the time delay of the median filter is w. A window size of 17 is used herein, with a delay of 8 samples. The introduction of a median filter brings about two advantages: (i) the suppression of unwanted outliers, which are mostly caused by erroneously detected (or missed) R-wave peaks; (ii) to preserve sharp edges (i.e., onsets and terminations of AF episodes) without extensively blurring the context.
B. Integer filter for low scale reference
where, the gain is G a i n 1=16=2^{4}, and the intrinsic delay of H _{ l }(z) is 7.5 samples. This low-pass filter is applied to smooth y _{ n } resulting from the previous median filtering. Another benefit of the low-pass filter is the removal of fluctuations possibly caused by Respiratory Sinus Arrhythmia (RSA) phenomena around the current sample from acquisition. Let xl _{ n } be the output of this filter, as illustrated in Figure 1(b).
C. Integer filter for high scale reference
where, the gain is G a i n 2=2048=2^{11}, and the relevant delay of H _{ h }(z) is 47 samples. This low-pass filter is introduced to generate a reference RR sequence of a larger scale, which needs to be exploited in the definition of symbolic series as explained in the following subsection. The resulting output denoted by xh _{ n } is shown in Figure 1(c).
As we have seen, the time delays of x _{ n } and xl _{ n } are −62.5 and −47 samples with respect to xh _{ n }, respectively. To ensure synchronization of the filtered data, let ${x}_{n}^{\prime}$ and ${\mathit{\text{xl}}}_{n}^{\prime}$ denote the corresponding time-delay corrected sequences of x _{ n } and xl _{ n }, respectively. Then, $\Delta {\mathit{\text{RR}}}_{n}={x}_{n}^{\prime}-{\mathit{\text{xl}}}_{n}^{\prime}$ can be defined as the difference in time delay, seen in Figure 1(d).
Symbolic dynamics of Δ RR _{ n }
The raw RR sequence x _{ n } is then quantified into symbol sequence sy _{ n } with specific symbols from the predefined “alphabet” in Eq. (4) (i.e., 0 to 9). Recalling Figure 1(a)-(d) and scanning the distribution of calculated symbols in Figure 1(e), we confirm that most of normal beats are defined as zero symbols, and possible abnormal beats (arrhythmias, e.g., AF) are defined as non-zero symbols by the transform Eq. (4).
Shannon entropy
Currently, the dynamic A consists of all 127 consecutive word elements from wv _{ n−126} to wv _{ n } (the bin size in this case is N=127). By determining the characteristic set A and the relevant probability set P with these elements, we can thus calculate the SE ${\mathcal{\mathscr{H}}}^{\mathrm{\prime \prime}}(\mathbf{A})$. The presence of AF is then detectable, with the rhythm labeled AF if ${\mathcal{\mathscr{H}}}^{\mathrm{\prime \prime}}(\mathbf{A})$ exceeds a discrimination threshold, and otherwise non-AF, which can be seen in Figure 1(g). We utilize the training database to determine the optimal discrimination threshold by investigating various threshold settings which lie within the range [0.0, 1.0]; the best performing threshold of 0.353 is thus derived and employed for the performance assessment using different testing databases.
Key issues of online processing
From Eq. (1)–(5) and (8), outwardly, this AF detection technique poses computational challenges. However, these challenges can be overcome by implementing clever recursive algorithms with beat-by-beat, real-time processing.
A. Pseudo-recursive median filtering
The median filter in Eq. (1) can be implemented with a so-called pseudo-recursive method: for input x _{ i }, we define S={s _{ r } ↑:1≤r≤2w+1} as a sorted array of successive elements from x _{ i−2w−1} to x _{ i−1}, where the output y _{ i } is obtained by following steps ➊-➎ below,
➊ A Binary search technique is used to seek out the position m of the sample x _{ i−2w−1} which will depart from the window (i.e., s _{ m }=x _{ i−2w−1}. Simultaneously, x _{ i } will get into the window);
➋ The Binary search technique is applied again to search for the position t at which the input x _{ i } needs to be set (i.e., s _{ t }<x _{ i }≤s _{ t+1});
➌ From positions m to t, the current s _{ r } is replaced with the adjacent s _{ r ± 1} (the ’ _{±}’ indicates where the element is taken from the right or left, with the ’ _{+}’ and ’ _{−}’ symbols representing the element to the right and left, respectively);
➍ Replace the element s _{ t } with x _{ i };
➎ Median s _{ w+1} of the updated S becomes output y _{ i }.
B. Recursive implementation of integer filters
The above equation, Eq. (9) includes 1 integer addition, 3 integer subtractions as well as 1 integer right-shift operation, when xl _{ n }>>4 (as G a i n 1=2^{4}) to offset the gain of H _{ l }(z).
where, xh _{ n−1}×2 is implemented with xh _{ n−1}<<1. The above equation, Eq. (10) consists of 2 integer additions, 8 integer subtractions, 1 integer left-shift operation and 1 integer right-shift operation, when xh _{ n }>>11 (as G a i n 2=2^{11}) to offset the gain of H _{ h }(z).
C. Mapping the definition of $-\frac{1}{\underset{2}{log}N}{p}_{i}\underset{2}{log}{p}_{i}$
where, C o n s=1000000 is a constant such that decimal floating points can be converted into integers and N=127, and $\stackrel{\lfloor \xb7\rfloor}{=}$ indicates to take the integer part of each $-\frac{\mathit{\text{Cons}}}{\underset{2}{log}N}{p}_{i}\underset{2}{log}{p}_{i}$.
Notably, for each cardiac cycle screened, this predefined PiMap permits the sole operation by picking the straightforward integer (i.e., P i M a p[i]) from the set PiMap in accordance with the index i rather than calculating $-\frac{1}{\underset{2}{log}N}{p}_{i}\underset{2}{log}{p}_{i}$ using arithmetic and logarithmic operations. The use of this predefined calculation significantly decreases calculation times.
D. Recursive implementation of ${\mathcal{\mathscr{H}}}^{\mathrm{\prime \prime}}(\mathbf{A})$
where $s{h}_{n}^{\prime}$ and $s{h}_{n}^{\mathrm{\prime \prime}}$ represent ${\mathcal{\mathscr{H}}}^{\prime}(\mathbf{A})$ and ${\mathcal{\mathscr{H}}}^{\mathrm{\prime \prime}}(\mathbf{A})$, respectively; ^{(∗} indicates that P i M a p[i]=0 is fixed for the case i≡0; and 127000000=N∗C o n s=127×1000000. For the next input wv _{ n+1}, steps âž€-âž‚ are again executed to obtain $s{h}_{n+1}^{\mathrm{\prime \prime}}$. From an online processing perspective, the time delays of $s{h}_{n}^{\mathrm{\prime \prime}}$ are 64 and 126.5 samples with respect to xh _{ n } and x _{ n }, respectively.
Materials and evaluation
Clinical ECG data sets
Four publicly-accessible sets of clinical data are selected for evaluation
Databases | f _{ s } | Total beats | Brief description |
---|---|---|---|
(Hz) | (AF beats) | ||
LTAFDB | 128 | 8996056 | It consists of 84 long-term (typically 24 to 25 hours) ECG |
(5326145) | recordings of subjects with paroxysmal or sustained AF. | ||
AFDB | 250 | It contains 25 long-term (10 hours) ECG recordings of subjects | |
1221574 | with AF (mostly paroxysmal). Of which raw ECG data of two | ||
(519687) | records (“00735” and “03665”) are not available, and two | ||
records (“04936” and “05091”) include many incorrect reference annotations | |||
MITDB | 360 | 109590 | It is a collection of 48 half-hour two-lead recordings which were |
(11496) | arrhythmia obtained from 47 subjects and contains affluent | ||
information, such as AF and AFL | |||
NSRDB | 128 | 1729523 | It includes 18 long-term records of subjects. Each recording is |
(0) | about 24 hours in duration. These records had no significant | ||
arrhythmias detected in this database |
Performance metrics
where, for a specific data set, TP (true positive) is the number of beats in AF which are correctly detected as AF, TN (true negative) is the number of beats in non-AF which are correctly detected as non-AF, FP (false positive) is the number of beats in non-AF which are incorrectly detected as AF, and FN (false negative) is the number of beats in AF which are incorrectly detected as non-AF. The proportion of beats in true AF which are correctly identified as AF is represented by Se, while Sp represents the proportion of beats in true non-AF which are correctly identified as non-AF, PPV represents the proportion of algorithm results that are true positive, and ACC represents the overall accuracy of our method. We consider Se and Sp as the main metrics, while PPV and ACC are complementary.
Results and discussion
Statistical results of this method for three testing databases (at the threshold of 0.353)
Method | Features | Year | Database | Key techniques | Results | |||
---|---|---|---|---|---|---|---|---|
Se(%) | Sp(%) | PPV(%) | ACC(%) | |||||
This method | RRI | 2013 | AFDB | 96.89 | 98.25 | 97.62 | 97.67 | |
AFDB^{‡} | Nonlinear | 96.82 | 98.06 | 97.61 | 97.50 | |||
AFDB^{†} | filter + integer | 97.83 | 98.19 | 97.56 | 98.04 | |||
MITDB | filters + symbolic | 97.33 | 90.78 | 55.29 | 91.46 | |||
NSRDB | dynamics + SE | NA | 98.28 | NA | NA | |||
AFDB+NSRDB | 96.89 | 98.27 | 92.30 | 98.03 | ||||
AFDB^{†}+NSRDB | 97.53 | 98.26 | 90.09 | 98.16 |
Overview of published results of the existing methods using the same databases
Method | Features | Year | Database | Key techniques | Results | |||
---|---|---|---|---|---|---|---|---|
Se(%) | Sp(%) | PPV(%) | ACC(%) | |||||
Lee, et al [25] ^{*} | RRI | 2013 | AFDB^{†}+NSRDB | Sample entropy | 97.26 | 95.91 | – | 96.14 |
Huang, et al [23] | RRI | 2011 | AFDB | Histogram+SD analysis+... | 96.1 | 98.1 | – | – |
NSRDB | NA | 97.9 | NA | NA | ||||
Lake, et al [22] | RRI | 2011 | AFDB | COSEn | 91 | 94 | – | – |
Lian, et al [21] ^{*} | RRI | 2011 | AFDB | Map of RdR | 95.8 | 96.4 | – | – |
MITDB | 98.9 | 78.8 | – | – | ||||
NSRDB | NA | 90.0 | NA | NA | ||||
Parvaresh, et al [20] ^{*} | AR | 2011 | AFDB^{‡} | LDA classifier | 96.14 | 93.20 | 90.09 | – |
Babaeizadeh, et al [16] | RRI/AA | 2011^{⋆} | AFDB^{‡} | Markov | 87.27^{⋆} | 95.47^{⋆} | 92.75^{⋆} | – |
(FSA) | 2009 | 92 | – | 97 | – | |||
Couceiro, et al [15] | RRI/AA | 2011^{⋆} | AFDB^{‡} | Neural network classifier | 96.58^{⋆} | 82.66^{⋆} | 78.76^{⋆} | – |
(PWA/FSA) | 2008 | 93.8 | 96.09 | – | – | |||
Schmidt,et al [14] | RRI/AA | 2011^{⋆} | AFDB^{‡} | Markov+Templete matching+... | 89.20^{⋆} | 94.58^{⋆} | 91.62^{⋆} | – |
(PWA/FSA) | 2008 | |||||||
Tatento, et al [13] ^{*} | RRI | 2011^{⋆} | AFDB | Kolmogorov-Smirnov test | 91.20^{⋆} | 96.08^{⋆} | 90.32^{⋆} | – |
2001 | 94.4 | 97.2 | 96.0 | – | ||||
Slocum, et al [12] | AA | 2011^{⋆} | AFDB^{‡} | Power percentage | 62.80^{⋆} | 77.46^{⋆} | 64.90^{⋆} | – |
(PWA/FSA) | 1992 | |||||||
Dash, et al [11] | RRI | 2009 | AFDB^{†} | RMSSD+TPR+SE | 94.4 | 95.1 | – | – |
MITDB | 90.2 | 91.2 | – | – | ||||
Kikillus, et al [10] ^{*} | RRI | 2007 | AFDB+NSRDB | Histogram+DIFF.+pNN200 | 94.1 | 93.4 | – | – |
We first introduce the methods based on the variability of RR intervals (RRI) [10, 11, 13, 21–23, 25].
Kikillus, et al [10] conducted a Markov modeling (MM) technique to identify AF. The calculated test results of Se and Sp were 94.1% (+2.79%, values in parentheses are the differences between our results and the reported results, hereinafter the same) and 93.4% (+4.87%) for the AFDB+NSRDB database.
The method introduced by Dash, et al [11], relies on the combination of the root mean square of successive differences (RMSSD), the turning points ratio (TPR) and SE. The presence of AF using this method was considered if given conditions based on thresholds were satisfied. For the AFDB^{ † } database, the calculated Se and Sp values were 94.4% (+3.43%) and 95.1% (+3.09%), respectively; and 90.2% (+7.13%) and 91.2% (-0.42%) for the MITDB set, respectively. When compared to our method with respect to the MITDB set, the Sp is slightly better than our method, however, there is an unacceptably lower rate of AF identification Se.
Tatento, et al [13] presented a novel technique using the Kolmogorov-Smirnov test. By choosing the AFDB data set for evaluation, the calculated Se, Sp and PPV values were 94.4% (+2.49%), 97.2% (+1.05%) and 96.0% (+1.62%), respectively. Other researchers’ re-investigated corresponding values were 91.20% (+5.69%), 96.08% (+2.17%) and 90.32% (+7.30%) [19], respectively.
Lian, et al [21] developed an AF detector with its basis centered on the Map of RR intervals versus change of RR intervals (RdR). For the AFDB and MITDB sets, the Se and Sp values were 95.8% (+1.09%) and 96.4% (+1.85%), 98.9% (-1.57%) and 78.8% (+11.98%), respectively. The calculated Sp for the NSRDB database was 90.0% (+8.28%). By comparison, when tested on the MITDB set, the Se is slight higher than that of our new method; there is, however, a markedly lower rate of non-AF detection Sp.
An attractive approach to AF detection was initiated by Huang, et al [23]. It utilized a histogram of Δ RR _{ n } and standard deviation (SD) analysis. The calculated Se and Sp were 96.1% (+0.79%) and 98.1% (+0.15%), when the AFDB set was assessed. The calculated Sp was 97.9% (+0.38%) for the NSRDB database. It provided the closest performance to that of this newly proposed method, as can be seen in Tables 2 and 3.
Lee, et al [25] investigated three statistical techniques to determine the presence of AF, and the best performance achieved when Sample entropy was employed. Using the AFDB^{ † }+NSRDB data set, the calculated Se, Sp and ACC were 97.26% (+0.27%), 95.91% (+2.35%) and 96.14% (+2.02%), respectively.
Parvaresh, et al [20] evaluated three classifiers for AF screening by using autoregressive modeling (AR). Within this method, AR coefficients of 15-second segments of ECGs were taken as features. When tested with the AFDB^{ ‡ } set, the best performance occurred at the so-called LDA classifier: the calculated Se, Sp and PPV were 96.14% (+0.68%), 93.20% (+4.86%) and 90.09% (+7.52%), respectively.
Slocum, et al [12] published a method based on the reference of AA. The frequency spectrum analysis (FSA) of the remainder generated by canceling the ventricular activity from the surface ECG was applied for differentiating rhythms. Due to the lack of a constant phase relationship between the atrial and ventricular activities, the performance of this type of technique is not high. The AF detection method based only on AA showed inferior performance as can be clearly seen from the Table 3: evaluated on the AFDB^{ ‡ } set, the calculated Se, Sp and PPV values were 62.80% (+34.02%), 77.46% (+20.60%) and 64.90% (+32.71%), respectively [19].
In summary, the results of this study demonstrate that the combination of nonlinear/linear integer filters, symbolic dynamics and SE yields a robust detector. This new detector exhibited a higher detection rate than previous methods. This could possibly lead to incorporation into computerized ECG interpretation systems to improve the reliability of arrhythmia classification.
A special issue on computational complexity
The computation time of the processing of this method
Databases | Signal duration (sec) | Computation time (sec) ^{§} |
---|---|---|
LTAFDB | 6970560 (1936.27 hours) | 11.09 |
AFDB | 917052.96 (254.74 hours) | 1.445 |
AFDB^{‡} | 843688.72 (234.36 hours) | 1.353 |
AFDB^{†} | 843688.72 (234.36 hours) | 1.406 |
MITDB | 86666.67 (24.07 hours) | 0.116 |
NSRDB | 1574976 (437.49 hours) | 1.825 |
AFDB+NSRDB | 2492028.96 (692.23 hours) | 3.258 |
Benefits and limitations
In this study, we use a discrimination threshold of 0.353 for AF classification. Of note, from Figure 7, increasing in threshold value improves Sp but decreases Se. By contrast, the decreasing in threshold values improves Se but decreases Sp. A compromising solution is thus necessary, and this makes it easy for one to apply specific threshold settings to the concrete application. In spite of this, comparing the latest detection methods when testing with each database, we confirm that a discrimination threshold of 0.353 is adequate to permit better performance of this new method under various situations.
It is commonly asserted and accepted that there is a great deal of time-consuming routines involved in the assessment of AF due to the statistical analysis of irregular/chaotic arrhythmia characteristics. Dramatic benefits can be achieved with the implementation of this AF detector through properly designed recursive algorithms as well as a novel predefined set $-\frac{1}{\underset{2}{log}N}{p}_{i}\underset{2}{log}{p}_{i}$ for the calculation of ${\mathcal{\mathscr{H}}}^{\mathrm{\prime \prime}}(\mathbf{A})$, which may markedly reduce computational complexity.
The bin size N was set to 127 in this study because a small quantity of words inside a small bin (≪N), in general, might indeed reduce the accuracy of estimating the word wv _{ n } probability distribution [30]. However, for sporadic AF episodes of relatively short duration (e.g., ten seconds), it might incur false negative detection, and this may be a potential limitation. In this regard, it is an inherent technical difficulty that needs to be overcome in the future, though AF episodes of very short duration are rare in practice. Nevertheless, it is essential to remember this limitation.
Once again, as stated in the previous section, a small PPV calculated from the MITDB database implies that this newly proposed approach needs to be further refined towards a universally applicable method.
Conclusions
As currently available techiniques are only modestly effective in AF episode screening, we developed a fully automated detection method which aims to fulfill two essential needs: (i) earlier real-time identification of AF, and (ii) higher reliability of detection. Therefore, with a method available elsewhere for real-time R-wave detection [31], this newly proposed method could be used in intensive care units. The online realization is easy to implement and is computationally attractive as it consists of only several basic operations such as integer addition/subtraction, integer left/right -shifting, integer comparison, and multiplication and division lying only within $\frac{k}{127000000}\xb7$, as well as 1 floating-point comparison between ${\mathcal{\mathscr{H}}}^{\mathrm{\prime \prime}}(\mathbf{A})$ and the threshold for the rhythm classification. Several state-of-the-art methods have been briefly reviewed, along with their methodologies and detection accuracy. Our new method is evaluated and compared with these existing methods using the LTAFDB, AFDB, NSRDB, and MITDB databases under various situations. We have also presented explicit tables for quantitative assessment of the performance and computation times. Collectively, our results suggest that this AF detector outperforms the existing methods with respect to the performance metrics Se, Sp, PPV and ACC. It is also worth emphasizing that a few reference annotations of these data sets are themselves imprecise, just as in the AFDB set. Therefore, extensive sets of exact reference annotations are still needed for investigation.
Appendix
Please visit the “https://onedrive.live.com/?gologin=1&mkt=zh-CN#cid=498A9A3CCEE3B366&id=498A9A3CCEE3B366%21132” for the compiled C++ dynamic link library files or contact the author for them.
Declarations
Acknowledgements
This work was supported in part by the National Basic Research Program 973 (2010CB732606), the Guangdong Innovation Research Team Fund for Low-Cost Healthcare Technologies in China, the External Cooperation Program of the Chinese Academy of Sciences (GJHZ1212), the Key Lab for Health Informatics of Chinese Academy of Sciences, the Enhancing Program of Key Laboratories of Shenzhen City (ZDSY20120617113021359), and the Supportive Program of “Peacock Program” of Shenzhen City for GIRTF-LCHT Team.
Authors’ Affiliations
References
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