Catheter ablation has emerged as an effective treatment strategy for atrial fibrillation (AF) in recent years. During AF, complex fractionated atrial electrograms (CFAE) can be recorded and are known to be a potential target for ablation. Automatic algorithms have been developed to simplify CFAE detection, but they are often based on a single descriptor or a set of descriptors in combination with sharp decision classifiers. However, these methods do not reflect the progressive transition between CFAE classes. The aim of this study was to develop an automatic classification algorithm, which combines the information of a complete set of descriptors and allows for progressive and transparent decisions. We designed a method to automatically analyze CFAE based on a set of descriptors representing various aspects, such as shape, amplitude and temporal characteristics. A fuzzy decision tree (FDT) was trained and evaluated on 429 predefined electrograms. CFAE were classified into four subgroups with a correct rate of 81±3%. Electrograms with continuous activity were detected with a correct rate of 100%. In addition, a percentage of certainty is given for each electrogram to enable a comprehensive and transparent decision. The proposed FDT is able to classify CFAE with respect to their progressive transition and may allow objective and reproducible CFAE interpretation for clinical use.
Atrial fibrillation (AF) is the most common cardiac arrhythmia, which affects morbidity and mortality [6]. AF is therefore a major and growing expense for health systems [11]. Catheter ablation has emerged as an effective treatment strategy in the last years. Since the discovery of foci inside the pulmonary veins (PVs) as a trigger for paroxysmal AF in 1998 [12], the technique of pulmonary vein isolation (PVI) has been established with acceptable success rates [29]. However, clinical outcome of PVI in persistent and long standing persistent AF is poor. This suggests different underlying mechanisms [4, 39, 40] outside the PV regions: multiple random propagating wavelets, focal electrical discharges or breakthroughs, and localized reentrant activity with fibrillatory conduction [5]. However, differentiation during an electrophysiological (EP) study is limited as specific electrogram characteristics are not known. Ndrepepa et al. showed that persistent AF presents with shorter cycle lengths and more disorganized activity than paroxysmal AF [27]. Areas with complex fractionated atrial electrograms (CFAE) have been reported to potentially represent AF substrate sites [1, 24]. Nademanee defined CFAE as fractionated electrograms composed of two deflections or more, and/or as the perturbation of the baseline with continuous deflection of a prolonged activation complex and a median cycle length of <120 ms over a 10 s recording period [24]. This author was the first to address CFAE as a target for catheter ablation with respectable success rates [24], but their results have not been reproduced by other groups [32, 33]. At present, the definitions used for CFAE have been variable and our understanding of its mechanistic significance remains incomplete [20]. It has been shown that both, the prevalence and distribution of CFAE differed significantly when different CFAE definitions were utilized [21, 36, 43]. Furthermore, filter settings in clinical signal acquisition systems can strongly influence the appearance of measured signals [14]. Although, the integration of automatic CFAE detection algorithms in 3D mapping systems has facilitated CFAE site retrieval, they vary in definition and do not respect the progressive transition between the CFAE classes [33]. It is therefore of importance to work out the different variants of CFAE and to develop mathematical algorithms which respect the fuzziness of these electrograms.
Wells et al. classified bipolar atrial electrograms during AF into four groups. Whereas type 1 and type 2 describes more regularized AF, type 3 is completely disorganized and type 4 is anything in between [42]. Allessie et al. described the complete activation pattern of the atrial wall obtained by high resolution mapping, and classified AF types according to the number of wavelets activating the atrial wall [1].
The detection of signals containing CFAE patterns has been addressed in literature. CuestaFrau et al. [9] used sample entropy to differentiate between CFAE and nonCFAE signals despite the presence of measuring artifacts. Recurrence quantification analysis was successfully applied by Navoret and colleagues [26] in the same context. Recurring patterns in CFAE were focused upon by Ciaccio et al. [8], resulting in an approach using transform coefficients on synchronized AF data.
There are some atrial electrogram decision making algorithms described in the literature [2, 18, 30] which are mainly based on the Wells classification. Kordik [18] defined an array of signal features which leads to a measure of the fractionation of a given signal. A neural net classifies the signals into four CFAE classes. They worked on a database with 113 annotated electrograms. Nollo et al. [30] defined features to describe the state of organization of CFAE. The best features were chosen on the basis of their JeffriesMatusita distance. In a final step, a supportvectormachine classifies CFAE into three classes. Their database included 100 annotated electrograms. The limitations of these algorithms are, that they do not deliver a measure of certainty for the chosen classification.
The aim of this study was to develop an algorithm which addresses the fuzziness, i.e., the progressive transition between the CFAE classes, and presents a percentage of certainty of the selected subgroups. This will objectivize CFAE interpretation in a more reproducible and transparent way.
The study cohort consisted of 11 patients who underwent catheter ablation of AF. The local Ethics Committee approved this study (according to the declaration of Helsinki), and all patients gave written informed consent. Eight patients had persistent and three paroxysmal AF. During the EP study, the left atrium was reconstructed using a 3D mapping system (Ensite NavX, St. Jude Medical, St. Paul, MN, USA) and PVI was performed. After PVI, bipolar electrograms were recorded using circular multipolar mapping catheters (10 polar Lasso, Biosense Webster, Diamond Bar, CA, USA; 14 polar OrbiterPV, Bard Electrophysiology, Lowell, MA, USA; 14 polar Optima, St. Jude Medical, St. Paul, MN, USA). After the procedure the electrograms and xyzcoordinates were exported and retrospectively analyzed. All together 605 recordings were analyzed. Each had a length of 5 s and was sampled at 1.2 kHz. The data was prefiltered by the measurement system with a high pass at 30 Hz and a low pass at 300 Hz.
For automatic classification, the Wells’ criteria [42] were modified by changing type 3 and 4. This led to a classification with a continuous increase in complexity (Figure 1).
All electrograms were preprocessed in the same way. Baseline wandering and lowfrequency noise were extracted from the atrial electrograms using a discrete wavelet transformbased approach [16]. Based on the sample frequency (f_{s}) the signal is decomposed up to level n=ld(f_{s}) and the approximation of level n is set to zero. After this step, the signal is composed again. The high frequency disturbance is removed by a conventional Butterworth low pass filter of order 4 with a cut off frequency of f_{LP}=300 Hz [15]. After removing the noise by means of signal processing, the 605 stationary 5 s signals were classified by two physicians from different centers according to the above predefined classes (see Table 1). After the classification process, only electrograms with an unambiguous assignment were used to train and test the fuzzy decision tree (FDT). So finally the database consisted of 429 classified electrograms.
CFAEClass  MD A  MD B  Coinciding 

C0  156  154  144 
C1  107  143  84 
C2  263  191  148 
C3  79  117  53 
MD, physician; A and B
To optimize the automatic electrogram characterization multiple features were generated. In the following sections a list of descriptors is delineated, reflecting the mathematical characteristics of CFAE.
Time domain descriptors based on nonlinear energy operator: In sinus rhythm or other regularized atrial activity, intracardiac recordings are only presenting deflections if an excitation wave front propagates near the recording electrode. At baseline, no electrical activity is present. Therefore, the electrograms can be divided into segments with and without baseline crossings. Segments with baseline crossings were defined as active. Localization of active segments during AF is complicated, because they vary in length and number. A mathematical expression of active segments can be described as the energy, which forms the basis for time domain analysis.
Teager’s nonlinear energy operator [NLEO, (1)] [38] forms the basis of this descriptor. Adding an adaptive threshold on the low pass filtered NLEO enabled the separation of active and inactive segments [28, 38]. The output of the NLEO E_{n} can be considered as an indication of the energy of the signal x(n) and it is proportional to the frequency and amplitude of the signal. Based on this segmentation a set of descriptors is defined.
The sum of the length of all active segments during a 5 s recording divided by the total length of the signal is called “activity ratio” (AR, descriptor D10).
with L the length of the total signal, M the number of active segments and l_{i} the length of an active segment i. Other descriptors are the mean length of active segments (MLAS, descriptor D11) and the standard deviation of active segments (sdMLAS, descriptor D12). They are defined as
Five more descriptors are defined based on the found active segments. These are the number of active segments (NoAS, descriptor D14), the mean number of zero crossings per active segment (ZCAS, descriptor D16), the standard deviation of zero crossings per active segment (sdZCAS, descriptor D18), the mean number of local maxima per active segment (LocMaxAS, descriptor D15) and the standard deviation of local maxima per active segment (sdMaxAS, descriptor D17). To analyze the signal curve within an active segment, a method is used that interprets the output of the NLEO as a probability density function. The standard deviation of this function is a measure of the concentration of the signal curve in time direction. The mean of the standard deviation of all active segments per signal is the descriptor mean variance in time direction (MVarTD, descriptor D1).
Phase space descriptors: A combined view of the signal’s magnitude and the alteration at a position t_{i} is enabled by the phase space. Here, the derivative of the signal x′(t_{i}) is plotted over its magnitude x(t_{i}). To weight the alteration and the magnitude equally, both are normalized to their maximum. To analyze the distribution of the samples in phase space, the phase space is divided into circular regions. Therefore a maximal distance d_{max} to the point of origin is defined. The distance is calculated by
To be more robust against outliers d_{max} is defined as the mean of the 5% of the maximal distances (d_{0.05max})
The aim of this definition is to use enough samples to lower the influence of outliers to the outer boundary, but with a minimum of samples. The phase space is divided into four regions depending on the distance d_{max}. Region 1 encloses all sample values within a distance of 0.05 d_{max}. The border for region 2 is at 0.1 d_{max}, for region 3 at 0.2 d_{max}, and for region 4 at d_{max}. The number of samples in each region divided by the total number of samples is called “Phase Space Sample Ratio” (PSSR 1, PSSR 2, PSSR 3, PSSR 4) (descriptors D3–D6). Therefore, the PSSR will group periodic electrograms (e.g., sinus rhythm) usually into region 1. For chaotic or nonperiodic signals, the assignment is more random. To reflect this behavior a binary function is generated. This binary function equals “1”, if a sample is located in region 4 and “0” otherwise. As the entropy mirrors the information content of a random process, it is suitable to measure the periodicity of this binary function. The entropy of the binary function is termed as entropy of phase space (EPS 4, descriptor D7). To evaluate the periodicity of the binary function, the standard deviation of distances between two adjacent rising edges is calculated. The resulting descriptor is called mean cycle length of phase space (MCPS 4, descriptor D8).
Wavelet based descriptors: Fractionated activity exhibits complex and time varying morphology. This is reflected by the broad frequency spectrum present in those electrograms. Using a wavelet based approach allows the analysis of different frequencies on multiple scales.
According to the algorithm presented in [17] a descriptor is set up, that counts the zero crossings in a given signal as a measure for the fractionation of this signal. Therefore, the signal is decomposed into coefficients with wavelet Coiflet 4 up to level 10. As shown by [19] Coiflet 4 is a suitable wavelet to analyze fractionation of electrograms. The algorithm can detect regions with high magnitude and high slew rate. As intracardiac electrograms often differ in terms of frequency, the wavelet level with the largest magnitude is searched. According to the algorithm from [17] this level is multiplied with the two preceding (higher frequency) levels. As a result, active regions will be emphasized and inactive regions will be suppressed. In the resulting signal, x_{res}, fractionated regions will be detected by a search for zero crossings. The number of zero crossings in this wavelet based electrograms are used as a descriptor (FracSig, descriptor D13).
Similarity of active segments: Faes et al. [10] described an algorithm that compares the similarity of different regions of an intracardiac electrogram. Inspired by Faes’ work a similarity analysis is computed. The envelope of the absolute value of the analytical signal is calculated according to the algorithm described in [34]. The envelope follows the shape of the signal, but is always positive. This approach respects the increasing variation of the accurate shape in a more fractionated electrogram. Active segments are extracted using the NLEObased segmentation algorithm. With these extracted segments a correlation matrix is built. First the segments are aligned using crosscorrelation. On the overlapping parts, the absolute value of the correlation coefficient is calculated. On the basis of this correlation matrix, a clustering is performed. Starting with cluster of the two most similar segments, step by step the nearest similar segment is added and the similarity between the new segment and the cluster is computed. According to the definition of Kaufman and Rousseeuw [13] the similarity between two clusters is calculated from
where R and Q are the cardinalities of both clusters, s(i, j) is the similarity between element i from cluster R and element j from cluster Q. Here, the number of elements of cluster Q is set to 1. Finally, the mean value of the similarities of an active signal (Similarity AS, descriptor D9) is computed.
Amplitude statistics based descriptors: The histogram of an electrogram indicates the measured values during the time of recording. Little electrical activity in atrial electrograms is resulting in an amplitude around zero. So, the amplitude histogram has a high peak around “0”, whereas the amplitude distribution of electrograms with strong electrical activity is spread more widely and is more gaussianlike. This behavior can be described with the fourth standardized moment, the kurtosis [25]. To lower the impact of outliers the signal is divided into k segments with a length l_{s}. The global kurtosis (HistKurt, descriptor D2) is averaged over the kurtosis of the k segments. The segment length l_{s} is set to 1 s to make sure that even in sinus rhythm up to a heart frequency of 1 Hz there is at least one atrial excitation in this segment.
Table 2 gives an overview of the 18 descriptors, most of them used by the FDT for classification of the electrograms.
Descriptor  CFAE 0  CFAE 1  CFAE 2  CFAE 3  


iqr 

iqr 

iqr 

iqr  
D1  MVarTD  0.17  (0.01)  0.18  (0.02)  0.21  (0.02)  0.23  (0.03) 
D2  HistKurt  22.62  (10.36)  12.88  (5.33)  9.76  (3.17)  6.83  (2.48) 
D3  PSSR 1  0.88  (0.08)  0.70  (0.14)  0.52  (0.11)  0.35  (0.12) 
D4  PSSR 2  0.02  (0.03)  0.07  (0.03)  0.13  (0,03)  0.14  (0.03) 
D5  PSSR 3  0.01  (0.01)  0.04  (0.02)  0.08  (0.02)  0.11  (0.02) 
D6  PSSR 4  0.10  (0.03)  0.18  (0.09)  0.26  (0.08)  0.40  (0.13) 
D7  EPS 4  1.60  (0.19)  2.09  (0.44)  2.51  (0.26)  2.67  (0.08) 
D8  MCPS 4  5.71  (0.32)  5.14  (0.31)  4.74  (0.20)  4.59  (0.14) 
D9  Similarity AS  0.17  (0.42)  0.68  (0.26)  0.97  (0.17)  1.17  (0.21) 
D10  AR  0.18  (0.06)  0.32  (0.15)  0.56  (0.17)  0.82  (0.14) 
D11  MLAS [ms]  54.46  (13.42)  75.67  (31.97)  132.09  (62.60)  312.88  (271.54) 
D12  sclMLAS [ms]  4.45  (13.95)  18.01  (13.58)  86.25  (78.23)  345.59  (334.66) 
D13  FracSig  345.00  (112.00)  630.00  (273.50)  809.00  (265.00)  1152.00  (433.00) 
D14  NoAS  18.00  (5.00)  26.00  (5.00)  24.00  (4.00)  15.00  (9.25) 
D15  LocMaxAS  1.68  (0.29)  2.02  (0.41)  2.52  (0.40)  3.29  (0.78) 
D16  ZCAS  1.44  (0.34)  1.76  (0.42)  2.27  (0.41)  3.06  (0.76) 
D17  sdMaxAS  0.18  (1.76)  1.48  (1.01)  4.21  (1.69)  6.54  (2.03) 
D18  sdZCAS  0.13  (1.78)  1.40  (1.01)  3.68  (1.70)  6.07  (2.09) 
Median (
Building the fuzzy decision tree: As a major advantage, FDT avoids sharp split nodes but instead assigns objects in a region around the split point a relative affiliation to the following child nodes. This fuzzy zone is realized using sigmoid functions of form:
as proposed by Chandra and Varghese [7]. z_{l} and z_{r} are the affiliation to the left and the right child node, s is the split point and v_{ω} is the descriptor value of descriptor ω. The coefficient σ is the standard deviation of this descriptor on the training data. If the descriptor values are scaled with a factor a, the width of the fuzzy zone is changing by a^{2}. This is not intended.
In this decision tree, we defined a coefficient k to replace σ. The beginning z_{start} of the fuzzy zone is set when the sigmoid function equals 0.01 and the end z_{end} is set when the sigmoid function equals 0.99. In general z_{start} (x)=g and z_{end} (x)=1–g. The range of a descriptor is the interval [x_{min}, x_{max}]. The width of the fuzzy zone is chosen as part p∈[0, 1] of the interval length i=x_{max}–x_{min}. It is assumed that the fuzzy zone with width p·i is set symmetrically to the split point s. This leads to
The sigmoid function is then expressed as
Inserting (10) in (11) and solving for k results in
with k_{1}=k_{2} one has
The boundaries for the interval [x_{min}, x_{max}] have to be determined from the training data. To be robust against outlier, the upper boundary value is set to 1.5 interquartile range of the upper quartile and the lower boundary to 1.5 interquartile range of the lower quartile. While training the decision tree, the aim is to find a split point at a node t, so that the relative membership of the objects to the right class is increasing. The Gini diversity index (GDI) is a regular criterion to evaluate possible split points [3]. The adaption of the GDI for usage in a FDT was made by Chandra et al. [7]:
where J is the number of classes;
sort descriptor values in descending order,
set the positions where the class affiliation is changing as candidates for a split point,
set the split point to the arithmetic mean of both neighboring descriptor values.
According to this algorithm for each descriptor vector the split point candidates are found. Finally, the candidate with the smallest GDI is chosen. The width of the fuzzy zone can be varied according to the distribution of classes over a descriptor. If there is a large overlap, the fuzzy zone should be larger than in the case of separated classes.
In this work, the best width of the fuzzy zone was determined by choosing the best result from a set of different fuzzy zones (Figure 2). For each fuzzy zone a 10×10 cross validation was realized and the mean error rate for each fuzzy zone was computed. The best error rate was achieved for a fuzzy zone with a width of 20%.
To make a decision on the class belonging of a given record, the decision tree needs an inference instruction. In contrast to a decision tree with sharp split values, where a record will get the class membership of the leaf node to which it is assigned, here, a test record can be assigned to more than one leaf node by a FDT. The inference instruction for a given record X_{Test} considers the fuzzy membership to a leaf node z(t_{i}, X_{Test}) and its class membership c(t_{i}). The fuzzy membership of a leaf node results from the product of fuzzy memberships of the passed nodes. The total class membership of a record X_{Test} is defined as
The classification of a record results in partial affiliation to the CFAE classes. From these partial affiliations, the resulting CFAE class is derived by a majority decision. The classified record will get the CFAE class label of the class with the highest percentage. The percentage can also be shown as a measure of certainty.
Chandra and Varghese used different sharp stop criteria to limit the growth of the tree while training [7]. This technique has some limitations. Therefore, the stop criteria of this decision tree were given very weak definitions. This led to an oversized tree at first. In an additional step this oversized tree was optimized to the best size [3]. The aim of pruning was to optimize the tree in regard to the complexity and the predictive accuracy. A sequence of trees is produced by the use of costcomplexitypruning [3]. Out of this sequence the best tree is chosen by evaluating the error rate in consideration with the size of the FDT. The final tree for the classification in a clinical context will be generated on the whole set of available classification data. The best tree is chosen again after cross validation and optimization by evaluating the size and error rate.
The standard way to evaluate a classificator is to perform a 10×10 cross validation [3]. This means the data set has to be separated into 10 equal subsets. The tree is trained with nine subsets and tested with the remaining “unknown” subset. The correct rates and error rates are computed and rated. This “training, testing and evaluation” step has to be repeated until each of the 10 subsets was used to test the tree. This whole procedure is repeated 10 times with different randomly created subsets, which leads to a total of 100 trees that are evaluated.
To optimize this tree, the evaluation has not only been done on the CFAE database but also on datasets from the UCI Machine Learning Database [22]. The UCI database is a regular database to test and evaluate machine learning algorithms. From this database five data sets [Iris, Wine, Wisconsin Breast Cancer (WBC), Haberman’s Survival (HS), and Glas Identification (GI)] have been chosen to evaluate the FDT. Data sets with continuous feature values and multiclass assignments have been chosen to be most similar to the CFAE data. Finally the results have been compared to different decision tree algorithms (CART [3], SLIQ [23], C4.5 [35], FDTx [7]).
To evaluate the distribution of the CFAE classes in clinical cases, bipolar electrograms were recorded for 5 s with a 300 Hz filter setting. Anatomical and electrical information were exported from the system and the 3D anatomical shells as well as the corresponding electrograms were reconstructed. At first, the algorithm was set to detect and display the mean cycle length (CL) of AF (mean dV/dt), with a deflection width of 10 ms and a refractory period of 37 ms. Electrograms with a mean CL>120 ms were color coded in violet, 120–70 ms in rainbow colors and <70 ms in white. The resulting map was compared to the CFAE classification map using the FDT.
All descriptors have been calculated using the database described previously. In the training phase, the FDT chooses the best descriptors to separate the classes depending on the cost function (e.g., GDI). The results of the 18 most frequently used descriptors are shown in Table 2.
For each CFAE class the median (
On the one hand, with increasing atrial activity the signals have more deflections and zero crossings and the isoelectric line is vanishing. Hence, the amplitude histogram is broad and the HistKurt (D2) has small or negative values. Sinus rhythm electrograms, on the other hand, mainly present an isoelectric line with some deflection other than zero. Therefore, the amplitude histogram has a pronounced peak. In this case the kurtosis values will be larger than for the ones for the continuous activity. This behavior is reflected by descriptors D1 and D2. When the heart rhythm evolves from sinus rhythm to AF the similarity of activation patterns gets lost [37]. As a consequence the active segments are getting dissimilar (D9).
Class C0 and C1 electrograms contain mostly regions without alteration (isoelectric line is prominent). This type of signals will be represented primarily in the phase space in region 1 and 2, whereas CFAE class C2 and C3 will be more prominent in region 3 and 4. Also the alterations of C2 and C3 signals make them lie more in the outer limits of the phase space. Descriptors D3–D8 mirror this. Going from the origin to the boundary of the phase space the number of C0 samples is decreasing while the number of C3 samples are increasing. PSSR 3 (region 3) contains 1% of C0, 4% of C1, 8% of C2, and 11% of C3 signals. EPS 4 and MCPS 4 give an information of the presence of a CFAE class in phase space region 4. These descriptors reflect the same outcome as the descriptor PSSR. The descriptors D13, D15, D16, D17, and D18, which reflect the fractionation of the signal, are increasing from C0 to C3, too. This corresponds to the increasing atrial activity and the increasing number of active fractionated segments (Figure 3).
The FDT presented in this work was compared to the results of decision trees with sharp split values and the FDT by Chandra and Varghese (FDT_{x}) [7]. Table 3 gives an overview of the results. Presented is the error rate and standard deviation for each classifier after a 10×10 cross validation. The results for FDT_{x} and SLIQ were obtained from [7] and the results for CART and C4.5 were obtained from [31]. The decision trees CART, SLIQ, and C4.5 have sharp split values; the FDT by Chandra and Varghese uses a sigmoidal function as decision border. FDT is the tree presented in this work. In addition to the error rate, the fuzzy zone width for each data set is given. When comparing with other classifiers for the data sets Iris, Wine and WBC, the proposed FDT delivers comparable results. Focusing on the results of the Haberman’s Survival and the Glas Identification data sets, the FDT is significantly superior.
CART  SLIQ  C4.5  FDT_{x}  FDT  Fuzzy zone width  

Iris  93.5±0.8  98.0±3.2  95.1±0.6  98.0±3.2  96.1±4.8  0.10 
Wine  89.3±0.8  88.3±8.1  92.7±1.1  88.9±4.5  89.1±7.2  0.01 
WBC  –  93.3±4.8  –  96.4±4.9  92.4±3.6  0.20 
HS  –  65.8±10.8  –  72.6±7.8  74.4±7.2  0.05 
GI  67.7±1.6  65.0±16.0  68.6±2.0  68.6±8.7  69.4±8.7  0.10 
Shown are the error rate (%)±the standard deviation (%) after 10×10 cross validation. CART and C4.5 data are obtained from Olaru and Wehenkel [31], SLIQ and FDTx are obtained from Chandra and Varghese [7]. Wisconsin Breast Cancer (WBC), Haberman’s Survival (HS), Glas Identification (GI).
Applying a 10×10 cross validation on the presented FDT with a fuzzy zone width of 20% results in a mean correct rate of 80.65±3.3%. Correct rates for the different CFAE classes are C0: 83.1±4.4%, C1: 81.0±8.1%, C2: 75.8±8.4%, C3: 82.7±17.8%. The distribution of the wrong assignments of signals per class is shown in Table 4. Increasing the training data will lead to an increased accuracy of the FDT. The depicted tree in Figure 4 is chosen from the sequence of cross validated trees. This tree has a correct rate of 86.1% (Figure 4).
CFAE 0  CFAE 1  CFAE 2  CFAE 3  

CFAE 0  X  72  24  4 
CFAE 1  37  X  63  1 
CFAE 2  2  49  X  49 
CFAE 3  0  0  100  X 
Using the CFAE mean algorithm, areas with high atrial frequencies were located in different areas of the left atrium. A loose correlation between CFAE class 2 and 3 could be estimated. Interestingly, in the FDT map electrograms with continuous activity (C3) were surrounded by C2 and C1. C3 were present mainly around the PV ostia, the anterior wall and the inferior part of the posterior wall (Figure 5).
The ablation of persistent AF is still challenging. Up to date, intracardiac electrograms are the only information which can be obtained during the procedure. But these electrograms present a broad variation. Current detection algorithms for CFAE are mainly based on single characteristics but do not respect the full complexity of the electrograms. Therefore, it seems appropriate to use more than one descriptor. Based on the modified Wells’ criteria, we classified CFAE into four subgroups with a continuously increase in complexity (C0–C3). The FDT can be used to classify electrograms which present a progressive transition between the different classes. This overcomes the results sensitivity/vulnerability against small changes of the descriptor’s value, which is one of the main shortcomings of decision trees with sharp split points [7]. Instead of using sharp split points to separate classes, the FDT assigns test objects within the fuzzy zone to classes with a specific probability. In this tree, several descriptors are introduced to describe the different characteristics of CFAE. The tree classifies a given signal into one of the four groups and presents a percentage of certainty.
Teager’s NLEO was used to differentiate between active and passive segments of the electrograms with simultaneous consideration of frequency and amplitude. The MLAS (D11), sdMLAS (D12) as well as AR (D10) are increasing from C0–C3 which reflects a faster and more complex local activation. These algorithms are therefore able to distinguish between no activity and continuous activity. The NoAS (D14) can separate C0 and C3 from C1 and C2 and the EPS (D7) between C0 and C3. PSSR 3 (D5) and the AR (D10) are able to distinguish between C1 and C2.
In the training phase, 18 descriptors in 100 different trees were analyzed and the correct rate of the CFAE classification was analyzed.
The performance of the FDT improves the more data is used to train the tree. In this study 429 signals with coinciding interpretation were analyzed. The smallest group of CFAE classes was C3 with 53 electrograms. Therefore, a maximum of 53 electrograms could be used in each CFAE class for the cross validation. To overcome the problem that a 10×10 cross validation would lead into a statistical analysis with only 5 signals, we performed a 10×5 cross validation. Using this technique, 11 signals remained to test the tree.
The width of the fuzzy zone for the FDT was set to 20%. Although another model, based on the GDI for different kind of class contribution (normally and unequally distributed, different overlap regions), revealed small fuzzy regions. In our experience, an increased fuzzy zone improves the power of the FDT (Figure 2).
The evaluation by the cross validation process estimates the correct rate of the method. This value is correct whilst the training data set reflects reality data. To generate a FDT working with a new data set or in a clinical setting, the tree will be trained with all available data [41]. The correct rate for this new classifier can be estimated from the mean correct rate of the cross validation. Therefore, a mean correct rate of 81±3% can be expected. The depicted tree in Figure 4 is chosen from the sequence of cross validated trees. This tree has a correct rate of 86.1%. This reflects the possibility, that with a wider set of training data, the outcome of the FDT can still be improved. In addition to the classification results, a percentage of certainty is given for each electrogram.
Compared to other algorithms, the proposed tree presents similar correct rates for Iris, Wine, and WBC data sets. For the Haberman’s Survival and Glas Identification data sets the proposed FDT is significantly superior. The reason for that might be the more fuzzy features of these data sets. This implies the strength of the proposed tree on fuzzy data.
In clinical use, the algorithm is able to display four different CFAE subgroups. This enables a more transparent and objective way of CFAE interpretation. For CFAE ablation, the most promising electrograms present continuous activity. These electrograms can be detected in 100% of the cases and accentuated in the 3D map. However, the classification of the CFAE allows a more detailed evaluation and may improve the knowledge about the stability and instability of certain areas during AF. This may restrict the search for characteristic electrograms sustaining AF.
CFAE express an important element of the AF substrate. We propose a new algorithm for automatic CFAE classification. According to the modified classification by Wells et al., CFAE were divided into four subgroups [42]. The subgroups were defined as nonfractionated with high frequency, fractionated with periodic activity, instable electrograms with a mixture of periodic fractionated and periodic nonfractionated atrial electrograms, and continuous activity. The algorithm is based on a FDT including 18 descriptors. Given a set of training data, a FDT classifier can be constructed. The training algorithm automatically chooses the ideal combination of descriptors and split points to the classification of training data. Obviously, this can lead to a tree, which only uses a subset of the offered set of descriptors as shown in the example tree (Figure 4). Using this tree, CFAE were sorted to one of the subgroups with a correct rate of at least 81%±3%. Electrograms with continuous activity were detected correctly 100% of the time. In addition, a percentage of certainty is given for each electrogram. The FDT is therefore able to classify CFAE with respect to their progressive transition. This will objectify CFAE interpretation in a more reproducible and transparent way.
This is a retrospective analysis of electrograms recorded from patients with AF. Prospective studies are needed to evaluate the clinical impact of this algorithm.
The authors would like to thank Minh Phuong Nguyen for substantial contributions to this work.
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