ࡱ>  &(#$%s}Z_ 0d,bjbjss T51 T?!?!?!!lC$?!G&d[*q*q*q*2t{N~LZ\\\\\\i@\ s@2t\q*q*Iq* q*ZZ̷Lq*<©N^?!\F0Gp( Z  h\\G : Neuro-ANFIS Architecture for ECG Rhythm-Type Recognition Using Different QRS Geometrical-based Features M. R. Homaeinezhad1, 2, E. Tavakkoli1, 2, A. Afshar, 2, 3 ,S. Abbas Atyabi 2, 3, A. Ghaffari1, 2 1- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran. 2- CardioVascular Research Group (CVRG), K. N. Toosi University of Technology, Tehran, Iran. 3- Department of Mechanical Engineering, Islamic Azad University of Tehran, south branch , Tehran, Iran. List of Abbreviations ANFIS: adaptive network fuzzy inference system MF: membership function ECG: electrocardiogram DWT: discrete wavelet transforms SNR: signal to noise ratio ANN: artificial neural network MEN: maximum epochs number NHLN: number of hidden layer neurons RBF: radial basis function MLP-BP: multi-layer perceptron back propagation LR: learning rate FP: false positive FN: false negative TP: true positive P+: positive predictivity (%) Se: sensitivity (%) CPUT: CPU time MITDB: MIT-BIH arrhythmia database SMF: smoothing function FIR: finite-duration impulse response LBBB: left bundle branch block RBBB: right bundle branch block PVC: premature ventricular contraction APB: atrial premature beat VE: ventricular escape beat PB: paced beat VF: ventricular flutter wave CHECK#0: procedure of evaluating obtained results using MIT-BIH annotation files CHECK#1: procedure of evaluating obtained results consulted with a control cardiologist CHECK#2: procedure of evaluating obtained results consulted with a control cardiologist and also at least with 3 residents Abstract The paper addresses a new QRS complex geometrical feature extraction technique as well as its application for electrocardiogram (ECG) supervised hybrid (fusion) beat-type classification. To this end, after detection and delineation of the major events of ECG signal via a robust algorithm, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Then, the curve length of each excerpted segment is calculated and is used as the element of the feature space. To increase the robustness of the proposed classification algorithm versus noise, artifacts and arrhythmic outliers, a fusion structure consisting of three Multi Layer Perceptron-Back Propagation (MLP-BP) neural networks with different topologies and one Adaptive Network Fuzzy Inference System (ANFIS) were designed and implemented. To show the merit of the new proposed algorithm, it was applied to all MIT-BIH Arrhythmia Database records and the discrimination power of the classifier in isolation of different beat types of each record was assessed and as the result, the average accuracy value Acc=98.27% was obtained. Also, the proposed method was applied to 8 number of arrhythmias (Normal, LBBB, RBBB, PVC, APB, VE, PB, VF) belonging to 19 number of the aforementioned database and the average value of Acc=98.08% was achieved. To evaluate performance quality of the new proposed hybrid learning machine, the obtained results were compared with similar peer-reviewed studies in this area. Keywords: Feature Extraction; Curve Length Method; Multi Layer Perceptron; Adaptive Network Fuzzy Inference System; Fusion (Hybrid) Classification; Arrhythmia Classification; Supervised Learning Machine. A. Introduction Heart is a special myogenic muscle which its constitutive cells (myocytes) possess two important characteristics namely as nervous (electrical) excitability and mechanical tension with force feedback. The heart's rhythm of contraction is controlled by the sino-atrial node (SA node) called the heart pacemaker. This node is the part of the hearts intrinsic conduction system, made up of specialized myocardial (nodal) cells. Each beat of the heart is set in motion by an electrical signal from the SA node located in the hearts right atrium. The automatic nature of the heartbeat is referred to as automaticity which is due to the spontaneous electrical activity of the SA node. The superposition of all myocytes electrical activity on the skin surface causes a detectable potential difference which its detection and registration together is called electrocardiography [1]. However the hearts electrical system controls all the events occurring when heart pumps blood. So if according to any happening, the electro-mechanical function of a region of myocytes encounters a failure, the corresponding abnormal effects will appear in the electrocardiogram (ECG) which is an important part of the preliminary evaluation of a patient suspected to have a heart-related problem. Based on a comprehensive literature survey among many documented works, it is seen that several features and extraction (selection) methods have been created and implemented by authors. For example, original ECG signal [17], preprocessed ECG signal via appropriately defined and implemented transformations such as discrete wavelet transform (DWT), continuous wavelet transform (CWT) [21], Hilbert transform (HT) [64], fast Fourier transform(FFT) [48-49], short time Fourier transform (STFT) [10], power spectral density (PSD) [51-52], higher order spectral methods [46-47], statistical moments [24], nonlinear transformations such as Liapunov exponents and fractals [43-45] have been used as appropriate sources for feature extraction. In order to extract feature(s) from a selected source, various methodologies and techniques have been introduced. To meet this end, the first step is segmentation and excerption of specific parts of the preprocessed trend (for example, in the area of the heart arrhythmia classification, ventricular depolarization regions are the most used segments). Afterwards, appropriate and efficient features can be calculated from excerpted segments via a useful method. Up to now, various techniques have been proposed for the computation of feature(s). For example mean, standard deviation, maximum value to minimum value ratio, maximum-minimum slopes, summation of point to point difference, area, duration of events, correlation coefficient with a pre-defined waveform template, statistical moments of the auto (cross) correlation functions with a reference waveform [32], bi-spectrum [46], differential entropy [37], mutual information [39], nonlinear integral transforms and some other more complicated structures [33-45] may be used as an instrument for calculation of features. After generation of the feature source, segmentation, feature selection and extraction (calculation), the resulted feature vectors should be divided into two groups train and test to tune an appropriate classifier such as a neural network, support vector machine or ANFIS, [30-40]. As previous researches show, occurrence of arrhythmia(s) affects RR-tachogram and Heart Rate Variability (HRV) in such a way that these quantities can be used as good features to classify several rhythms. Using RR-tachogram or HRV analysis in feature extraction and via simple if-then or other parametric or nonparametric classification rules [7-9], artificial neural networks, fuzzy or ANFIS networks [10-14], support vector machines [15] and probabilistic frameworks such as Bayesian hypotheses tests [16], the arrhythmia classification would be fulfilled with acceptable accuracies. Heretofore, the main concentration of the arrhythmia classification schemes has been on morphology assessment and/or geometrical parameters of the ECG events. Traditionally, in the studies based on the morphology and the wave geometry, first, during a preprocessing stage, some corrections such as baseline wander removal; noise-artifact rejection and a suitable scaling are applied. Then, using an appropriate mapping for instance, filter banks, discrete or continuous wavelet transform in different spatial resolutions and etc., more information is derived from the original signal for further processing and analyses. In some researches, original and/or preprocessed signal are used as appropriate features and using artificial neural network or fuzzy classifiers [17-25], parametric and probabilistic classifiers [26-28], the discrimination goals are followed. Although, in such classification approaches, acceptable results may be achieved, however, due to the implementation of the original samples as components of the feature vector, computational cost and burden especially in high sampling frequencies will be very high and the algorithm may take a long time to be trained for a given database. In some other researches, geometrical parameters of QRS complexes such as maximum value to minimum value ratio, area under the segment, maximum slope, summation (absolute value) of point to point difference, ST-segment, PR and QT intervals, statistical parameters such as correlation coefficient of a assumed segment with a template waveform, first and second moments of original or preprocessed signal and etc. are used as effective features [29-35]. The main definition origin of these features is based on practical observations and a priori heuristic knowledge whilst conducted researches have shown that by using these features, convincing results may be reached. On the other hand, some of studies in the literature focus on the ways of choosing and calculating efficient features to create skillfully an efficient classification strategy [36-39]. In the area of nonlinear systems theory, some ECG arrhythmia classification methods on the basis of fractal theory [40, 41], state-space, trajectory space, phase space, Liapunov exponents [42-44] and nonlinear models [45] have been innovated by researchers. Amongst other classification schemes, structures based on higher order statistics in which to analyze features, a two or more dimensional frequency space is constructed can be mentioned [46, 47]. According to the concept that upon appearance of changes in the morphology of ECG signal caused by arrhythmia, corresponding changes are seen in the frequency domain, therefore, some arrhythmia classifiers have been designed based on the appropriate features obtained from signal fast Fourier transform (FFT), short-time Fourier transform (STFT), auto regressive (AR) models and power spectral density (PSD), [48-53]. Finally, using some polynomials such as Hermite function which has specific characteristics, effective features have been extracted to classify some arrhythmias [54, 55]. The general block diagram of the proposed heart arrhythmia recognition-classification algorithm including two stages train and test is shown in Fig. 1. According to this figure, first, the events of the ECG signal are detected and delineated using a robust wavelet-based algorithm [62-63]. Then, each QRS region and also its corresponding DWT are supposed as virtual images and each of them is divided into eight polar sectors. Next, the curve length of each excerpted segment is calculated and is used as the element of the feature space and to increase the robustness of the proposed classification algorithm versus noise, artifacts and arrhythmic outliers, a fusion structure consisting of three MLP-BP neural networks with different topologies and one ANFIS were designed and implemented. The new proposed algorithm was applied to all 48 records of the MIT-BIH Arrhythmia Database (MITDB) and the average value of Acc=98.27% was obtained. Also, the proposed hybrid classifier was applied to 8 number of arrhythmias (Normal, LBBB, RBBB, PVC, APB, VE, PB, VF) belonging to 19 number of the MITDB and the average value of Acc=98.08% was achieved. To compare the outcomes with previous peer-reviewed studies and to show the generalization power of the proposed classification algorithm, 4,011 and 4,068 samples have been selected for training and for testing groups, respectively.  SHAPE \* MERGEFORMAT  Figure 1. The general block diagram of an ECG beat type recognition algorithm supplied with the virtual image-based geometrical features B. Materials and Methods B.1. The Discrete Wavelet Transform (DWT) Generally, it can be stated that the wavelet transform is a quasi-convolution of the hypothetical signal  EMBED Equation.3  and the wavelet function  EMBED Equation.3  with the dilation parameter  EMBED Equation.3  and translation parameter  EMBED Equation.3 , as the following integration  EMBED Equation.3  (1) The parameter  EMBED Equation.3  can be used to adjust the wideness of the basis function and therefore the transform can be adjusted in several temporal resolutions. In Eq. 1, for dilation parameter a and the translation parameter b, the values  EMBED Equation.3  and  EMBED Equation.3  can be used in which q is the discretization parameter, l is a positive constant, k is the discrete scale power and T is the sampling period. By substituting the new values of the parameters a and b into the wavelet function EMBED Equation.3 , the following result is obtained  EMBED Equation.3  (2) The scale index k determines the width of wavelet function, while the parameter l provides translation of the wavelet function. If the scale factor  EMBED Equation.3  and the translation parameter  EMBED Equation.3  are chosen as q=2 i.e.,  EMBED Equation.3  and  EMBED Equation.3 , the dyadic wavelet with the following basis function will be resulted [76],  EMBED Equation.3  (3) To implement the a trous wavelet transform algorithm, filters  EMBED Equation.3  and  EMBED Equation.3  should be used according to the block diagram represented in Fig. 2-a, [76]. According to this block diagram, each smoothing function (SMF) is obtained by sequential low-pass filtering (convolving with  EMBED Equation.3  filters), while after high-pass filtering of a SMF (convolving with  EMBED Equation.3  filters), the corresponding DWT at appropriate scale is generated. In order to decompose the input signal x(t) into different frequency passbands, according to the block diagram of Fig. 2-b, sequential high-pass low-pass filtering including down-sampling should be implemented. The filter outputs  EMBED Equation.3  and  EMBED Equation.3  can be obtained by convolving the input signal  EMBED Equation.3  with corresponding high-pass and low-pass finite-duration impulse responses (FIRs) and contributing the down-sampling as  EMBED Equation.3  (4) On the other hand, to reconstruct the transformed signal, the obtained signals  EMBED Equation.3  and  EMBED Equation.3  should be first be up-sampled by following simple operation  EMBED Equation.3  (5) If the FIR lengths of the H(z) and G(z) filters are represented by  EMBED Equation.3  and  EMBED Equation.3 , respectively, then the reconstructing high-pass and low-pass filters are obtained as  EMBED Equation.3  (6) Then the reconstructed signal  EMBED Equation.3  is obtained by superposition of the up-sampled signals convolution with their appropriately flipped FIR filters as follow  EMBED Equation.3  (7) For a prototype wavelet  EMBED Equation.3  with the following quadratic spline Fourier transform,  EMBED Equation.3  (8) the transfer functions  EMBED Equation.3  and  EMBED Equation.3  can be obtained from the following equation  EMBED Equation.3  (9) and therefore,  EMBED Equation.3  (10) It should be noted that for frequency contents of up to 50 Hz, the a trous algorithm can be used in different sampling frequencies. Therefore, one of the most prominent advantages of the a trous algorithm is the approximate independency of its results from sampling frequency. This is because of the main frequency contents of the ECG signal concentrate on the range less than 20 Hz [62-63]. After examination of various databases with different sampling frequencies (range between 136 to 10 kHz), it has been concluded that in low sampling frequencies (less than 750 Hz), scales 2 (=1,2,& ,5) are usable while for sampling frequencies more than 1000 Hz, scales 2 (=1,2,& ,8) contain profitable information that can be used for the purpose of wave detection, delineation and classification.  SHAPE \* MERGEFORMAT  Figure 2. FIR filter-bank implementation to generate discrete wavelet dyadic scales and smoothing functions transform based on a trous algorithm. (a) one-step generation of detail coefficient scales and reconstruction of the input signal, (b) four-step implementation of DWT for extraction of dyadic scales. B.2. ANFIS Classification Strategy ANFIS is a fuzzy Sugeno model of integration where the final fuzzy inference system is optimized via the ANNs training. ANFIS can be viewed as a class of adaptive networks which are functionally equivalent to fuzzy inference system. It maps inputs through input membership function and associated parameters , and then through output membership function to outputs. ANFIS uses back-propagation or a combination of least square estimation and back-propagation for membership function parameter estimation. The most important point in data classification by ANFIS is designing of fuzzy rules. To solve this problem, several clustering techniques such as fuzzy c-means (FCM), K-means clustering (KMC) and histogram adaptive smoothing (HAS) can be utilized. In this study, subtractive clustering is used in which each cluster represents one independent rule, (Jang, 1993 [71]).  EMBED Word.Picture.8  Figure 3. The general of ANFIS used for heart rhythm classification [71] B.2.1 Subtractive Clustering Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. Clustering algorithms are used extensively not only to organize and categorize data, but are also useful for data compression and model construction. Clustering techniques are used in conjunction with radial basis function networks or fuzzy modeling primarily to determine initial location for radial basis functions or fuzzy if-then rules. There are different clustering technique such as k-means clustering, fuzzy c-means clustering, mountain clustering and subtractive clustering. If there is no clear idea how many clusters there should be for a given set of data, subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and the cluster centers in a set of data. Consider a collection of n data points in an m-dimensional space. Without loss of generality, the data points are assumed to have been normalized within a hypercube. Since each data point is a candidate for cluster centers, a density measure at data point xi is defined as:  EMBED Equation.3  (11) Where ra is a positive constant. Hence a data point will have a high density value if it has many neighboring data points. The radius ra defines a neighborhood; data points outside this radius contribute only slightly to the density measure. After the density measure of each data point has been calculated, the data point with the highest density measure is selected as the first cluster center. Let xc1 be the point selected and Dc1 its density measure. Next the density measure for each data point xi is revised by the formula  EMBED Equation.3  (12) where rb is a positive constant. Therefore, the data points near the first cluster center xc1 will have significantly reduced density measures, thereby making the points unlikely to be selected as the next cluster center. The constant rb defines a neighborhood that has measurable reductions in density measure. The constant rb is normally larger than ro to prevent closely spaced cluster centers; generally rb is equal to 1.5 ra. After the density measure for each data point is revised, the next cluster center xc2 is selected and all of the density measures for data points are revised again. This process is repeated until a sufficient number of cluster centers are generated. When applying subtractive clustering to a set of input-output data, each of the cluster centers represents a prototype that exhibits certain characteristics of the system to be modeled. These cluster centers would be reasonably used as the centers for the fuzzy rules' premise in a zero-order Sugeno fuzzy model, or radial basis functions in an Radial Basis Function Network (RBFN). For instance, assume that the center for the i-th cluster is ci in an M dimension. The ci can be decomposed into two component vectors pi and qi, where pi is the input part and it contains the first N element of ci; qi is the output part and it contains the last M - N elements of ci. Then given an input vector x, the degree to which fuzzy rule i is fulfilled is defined by  EMBED Equation.3  (13) This is also the definition of the i-th radial basis function if we adopt the perspective of modeling using RBFNs. Once the premise part (or the radial basis functions) has been determined, the consequent part (or the weights for output unit in an RBFN) can be estimated by the least-squares method. After these procedures are completed, more accuracy can be gained by using gradient descent or other advanced derivative-based optimization schemes for further refinement, (Jang, 1993 [71]). C. The Neuro-ANFIS Fusion Classification Algorithm: Design, Implementation and Performance Evaluation C.1. QRS Geometrical Features Extraction C.1.1. ECG Events Detection and Delineation In this step, QRS complexes are detected and delineated. Today reliable QRS detectors based on Hilbert [64, 65] and Wavelet [62, 63] transforms can be found in literature. In this study, an ECG detection-delineation method with the sensitivity and positive predictivity Se = 99.95% and P+ = 99.94% and the average maximum delineation error of 6.1 msec, 4.1 msec and 6.5 msec for P-wave, QRS complex and T-wave, respectively is implemented [62]. By application of this method, detecting the major characteristic locations of each QRS complex i.e., fiducial, R and J locations, becomes possible. C.1.2. Detected QRS Complex Geometrical Features Extraction [77] In order to compute features from the detected QRS complexes either normal or arrhythmic via the proposed method, first a reliable time center should be obtained for each QRS complex. To find this point, the absolute maximum and the absolute minimum indices of the excerpted DWT dyadic scale 24 using the onset-offset locations of the corresponding QRS complex, are determined. It should be noted that according to comprehensive studies fulfilled in this research, the best time center of each detected QRS complex is the mean of zero-crossing locations of the excerpted DWT (see Fig. 4).  Figure 4. Determination of the time center of a detected QRS complex using excerpted DWT scale 24 To make a virtual close-up from each detected QRS complex, a rectangle is built on the complex with following specifications: The left-side mid-span of the rectangle is the fiducial location of the QRS complex. The Absolute distance of the complex from the fiducial point is the half of the rectangle height. The center of rectangle is the time-center of the QRS complex. The right-hand abscissa of the rectangle is the distance between QRS time center and its J-location. Afterwards, Each QRS region and also its corresponding DWT are supposed as virtual images and each of them is divided into eight polar sectors. Next, the curve length of each excerpted segment is calculated and is used as the elements of the feature space, (therefore, for each detected QRS complex, 16 features are computed). The quantity curve-length of a hypothetical time series x(t) in a window with length WL samples can be estimates as  EMBED Equation.3  (14) Where,  EMBED Equation.3  is sampling frequency of the time series x(t). The curve length is suitable to measure the duration of the signal  EMBED Equation.3  events, either being strong or weak. Generally, the MCL measure indicates the extent of flatness (smoothness or impulsive peaks) of samples in the analysis window. This measure allows the detection of sharp ascending/descending regimes occurred in the excerpted segment [63]. In Fig. 1, the general block diagram of the ECG beats annotation algorithm with the proposed QRS geometrical feature space is illustrated. A generic example of a holter ECG and its corresponding 24 DWT dyadic scale with the virtual images of the complexes provided for feature extraction as well as two quantities obtained from the RR-tachogram are shown in Fig. 5.  (a) (b) (c)Figure 5. Extraction of the geometrical features from a delineated QRS complex via segmentation of each complex into 8 polar sectors by generating of a virtual image from the complex. (a) original ECG, (b) DWT of the original ECG and (c) RR-interval. C.2. Design of the Hybrid (Fusion) Neuro-ANFIS Classification Algorithm C.2.1. Design of the Particle Classifiers In the heart-beat classification context, due to differences existing in the theory and the structure of the several types of classifiers such as Artificial Neural Network (ANN) and ANFIS reasonably, achieving exactly similar result from them given a common train and test feature spaces, cant be expected. Assessments confirm that in the arrhythmia classification of the MITDB, even if the average discrimination power of an appropriately designed classifier is superior to another rival classifier, however, existence of some records in which exceptionally higher generated accuracies obtained from the rival classifier may be possible. In order to increase the total accuracy of the proposed classification algorithm, one way is to synthesize the output of several classification algorithms with different inherent structures to achieve the best accuracy as much as possible leading to higher robustness against uncertainties and probable arrhythmia or outliers. In this study, to build a fusion (hybrid) classification scheme, three MLP-BP with different topologies and one ANFIS are properly regulated using the train dataset. The specifications of each classification algorithm are described below. MLP-BP1. The first MLP-BP classifier includes one hidden layer with number of hidden layer neurons (NHLN) equal to 11 and tangent sigmoid and the logarithmic sigmoid as the activation functions of the hidden layer and output layer, respectively. Also, for this ANN, MEN is chosen to be 200. MLP-BP2. This classifier possesses one hidden layer with NHLN=12. The tangent sigmoid was chosen as the activation function for both hidden and output layers, respectively. For this ANN, MEN = 150 was assigned. MLP-BP3.The third MLP-BP classifier includes one hidden layer with logarithmic sigmoid as the activation function for both hidden and output layers, respectively. For this ANN, MEN = 300 and NHLN=14 was assigned. ANFIS. for generating fuzzy inference system, the parameters of subtractive clustering is set as follow: Range of influence=0.5, Squash factor=0.55, Accept ratio=0.5, Reject ratio=0.15. With these parameters, 7 fuzzy rules are obtained. It should be noticed that several parameters such as types of activation functions and several values for NHLN, MEN, Range of influence, Squash factor ,Accept ratio and Reject ratio were examined and were altered based on trying-and-error method and suitable ranges and types were chosen for these parameters. C.2.2. The Neuro-ANFIS Fusion Classification Scheme To design a fusion classification algorithm, after appropriate training of three MLP-BP classifiers, ANFIS is regulated to merge results of all particle classifiers. To train this classifier, obtained outputs of each MLP-BP are set as the train feature vector for ANFIS. In Fig. 6, the block diagram of the proposed fusion classification algorithm including different classifiers in the train and test stages is illustrated.  EMBED Word.Picture.8  Figure 6. Design of the fusion classification algorithm via merging the outpur of several pre-trained particle classifiers in ANFIS. To evaluate performance of the proposed feature extraction method and the fusion classification algorithm, the following steps are pursued Evaluation of the discriminate power of the selected features. Design of the particle classifiers and their implementation to all MITDB records. Design of the fusion classifier for each MITDB record and comparing the obtained results with each particle classifier. Selection of some rhythms from the MITDB records and designing of the particle and fusion classifiers. Comparison of the obtained final results with previous similar peer-reviewed studies. C.3. Results and Discussion In table 1, the numeric codes of the 23 MITDB rhythms and their corresponding annotations are illustrated. After implementation of the three MLP-BP neural network and ANFIS classifier and the corresponding fusion classifier to all 48 MITDB records and calculation of the accuracy, the obtained results are shown in table 2. According to this table, the ANFIS classifier yielded the average accuracy of Acc=98.27% given all data and all rhythms of the MITDB records. As it can be seen in this table, the overall performance quality associated with the ANFIS is superior rather than the each MLP classifier. Table 1. The different rhythm types and the corresponding equivalent ASCII code integer numbers Numeric CodeRhythmNumeric CodeRhythm33Ventricular Flutter Wave83Supraventricular Premature or Ectopic Beat34Comment Annotation86Premature Ventricular Contraction43Rhythm Change91Start of Ventricular Flutter/Fibrillation47Paced Beat93End of Ventricular Flutter/Fibrillation65Atrial Premature Beat97Aberrated Atrial Premature Beat69Ventricular Escape Beat101Atrial Escape Beat70Fusion of Ventricular and Normal Beat102Fusion of Paced and Normal Beat74Nodal (junctional) premature Beat106Nodal (junctional) Escape Beat76Left Bundle Branch Block Beat120Non-Conducted P-wave (Blocked APC)78Normal Beat124Isolated QRS-Like Artifact81Unclassifiable Beat126Change in Signal Quality82Right Bundle Branch Block Beat Table 2. Performance of the fusion classification algorithm for all MIT-BIH records  EMBED Word.Picture.8  In order to be able for comparing the obtained results of this study with the outcomes of the previous researches ([46, 72-75]), utilizing exactly the same train and test databases is mandatory. To this end, records 100,102,104,105, 106,107 109,111, 114, 116, 118, 119,124,200,207,209,212 , 214 and 217 are selected from the MITDB records and the rhythms Normal, left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), atrial premature beat (APB) , ventricular escape beat (VE) , paced beat (PB) and ventricular flutter wave (VF) are extracted according to the MITDB annotation files. In table 3, the name of the MITDB records as well as the selected rhythm types and their corresponding beat numbers are presented. Table 3. The name of selected MITDB records with their rhythm types contents for the aim of performance evaluation and comparison with other studies NormalLBBBRBBBPVCAPBVEPBVFtraintesttraintesttraintesttraintesttraintesttraintesttraintesttraintestrecord100146158000 000181500000010200000000000010010000104000000000000100100001051651790000121200000000106100107000015015000000000107000000000000100100001090018520100111100000000111001581710000000000001141191290000121255000000116150163000031310000000011800001932125553430000001191011090000127127000000001240000136150131300000000200114123000023623616140000002070010811788303058495550002722002091711850000002081740000002126065001631800000000000214001491610073730000000021700000000000010010000Total112612186006505005507007003583005550400400272200 C.2.2. Error Analysis It should be noted that if some diversely designed classification algorithms show error rate diversity relative to each other for a given common database, then the utilization of them in a fusion classification structure is justified. In Fig. 7, the error rate diversity of structural classifiers including three MLP-BP and ANFIS classifier is demonstrated. In table 4, the performance of the fusion classification algorithm has been described by the obtained confusion matrix. For instance, the fifth row of this table shows that 2, 3, 1, 4, 0, 1 and 0 beat numbers were falsely classified into the Normal, LBBB, RBBB, APB, VE, PB and VF categories, respectively. In this way the number of fusion classifier false negative (FN) detections for the PVC class equals to11. On the other hand, for instance, the third column of this table illustrates that 10, 4, 3, 0, 1,0 and 0 beat numbers from the Normal, RBBB, PVC, APB, VE, PB and VF categories, respectively were falsely classified as LBBB class, i.e., the number of fusion classifier false positive (FP) detections for the LBBB class equals is 18 .  Figure 7. Error-rate diversity analysis for justification of the fusion of Three MLP-BP and ANFIS classifiers Table 4. Performance evaluation of the presented classification algorithm for the selected MITDB records the confusion matrix Normal LBBB RBBB PVC APB VE PB VF Normal 1198 10 2 2 2 1 3 0 LBBB 5 639 1 3 1 0 0 1 RBBB 2 4 539 1 3 0 1 0 PVC 2 3 1 689 4 0 1 0 APB 1 0 1 7 288 2 1 0 VE 0 1 1 0 3 45 0 0 PB 2 0 1 1 0 2 394 0 VF 0 0 0 2 0 0 0 198  C.3. Arrhythmia Classification Performance Comparison with Other Works In the final step, in order to show the marginal performance improvement of the proposed arrhythmia hybrid classification algorithm, the method is assessed relative to other high-performance recent works. The result of comparison of the proposed method and other works is shown in table 5. Table 5. Performance evaluation of the presented fusion classification algorithm (a) Results obtained from several classification algorithms implemented in this study including three MLP and ANFIS classifiers. (b)summary of previous study (a) Result of this study Classifier sensitivity Total Accuracy (%) Normal LBBB RBBB PVC APB VE PB VF MLP-BP1 96.06 93.85 94.37 93.72 91.34 90 94.25 95.5 94.45 MLP-BP2 95.49 91.39 93.28 89.58 89 74 92.5 94 92.45 MLP-BP3 97.2 96.77 95.1 93.143 92.67 96 97 95.5 95.7 ANFIS 98.36 98.31 98 98.43 96 90 98.51 99 98.08  (b) Summary of previous study AuthorsMethodsignalDatasetAccuracyLinh and Osowski [78]Feature extraction: Hermite Coefficients Classification: anfis ECG7279 beats from MIT-BIH; 3611 training-3668 testing; [Normal: 2344, LBBB: 1250, RBBB: 1050, PVC:1400 , APB:658,VE:105, VF: 472]96 N.Kannathal and C.M. Lim [14]Feature extraction: Largest Lyapunov exponent, Spectral entropy, Poincare geometry Classification: anfisRR interval600 dataset from MIT-BIH; 320 training-280testing; 10 classes94Osowski and Linh [73]Feature extraction: cumulants of the second, third and fourth order Classification: fuzzy hybrid neural networkECG7185 beats from MIT-BIH; 4035 training3150 testing [Normal: 2250, APB: 658, LBBB: 1200, PVC: 1500, RBBB: 1000, VF: 472, VE: 105]96.06Dokur and Olmez [67]Feature extraction: discrete wavelet transform Classification: intersecting spheres networkECG3000 beats from MIT-BIH; Normal, LBBB, RBBB, P, p, a, VE, PVC, F, f: 300 from each category; 1500 training1500 testing 95.7S. N. Yu and Y. H. Chen[46]Feature extraction: higher order statistics of subband components Classification: feedforward neural network ECG 7185 beats from MIT-BIH; 4035 training3150 testing [Normal: 2250, APB: 658, LBBB: 1200, PVC: 1500, RBBB: 1000, VF: 472, VE: 105]97.53 Hu et al. [22]Feature extraction: PCA in 29 points from QRS, instantaneous and average RR-interval, QRS complex width Classification: mixture of experts (SOM, LVQ)ECG25 min from each record in MIT-BIH 200 series excluding records 212, 217, 220, 222 and 232 [Normal: 43897, PVC: 5363] 95.52 Tsipouras et al. [30]Feature extraction: RR-interval Classification: knowledge-based systemRRinterval signal 30000 beats from MIT-BIH [N, P, f, P, Q, LBBB, RBBB: 25188, PVC, F: 2950 , APB, a, J, S: 1213, e, j, n, VE: 265, VF: 384] 95.85  This study Feature extraction: Geometrical properties obtained from segmentation of each detected-delineated QRS complex virtual image as well as RR-tachogram Classification: A fusion structure consisting of three MLP and ANFIS classifiersECG 8079 beats from MIT-BIH; 4011 training4068 testing [Normal: 2344, LBBB: 1250, RBBB: 1050, PVC:1400 , APB:658,VE:105 ,PB:800, VF: 472]98.08  D. Conclusion In this study, a new supervised heart arrhythmia hybrid (fusion) classification algorithm based on a new QRS complex geometrical features extraction technique as well as an appropriate choice from each beat RR-tachogram was described. In the proposed method, first, the events of the ECG signal were detected and delineated using a robust wavelet-based algorithm. Then, each QRS region and also its corresponding DWT were supposed as virtual images and each of them was divided into eight polar sectors. Next, the curve length of each excerpted segment was calculated and is used as the element of the feature space. To increase the robustness of the proposed classification algorithm versus noise, artifacts and arrhythmic outliers, a fusion structure consisting of different classifiers namely three MLP-BP neural networks with different topologies and one ANFIS were designed. 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_`prv~'*-.1257;<=hG6hwd?B*CJaJphhFEhwd?B*CJaJphhwd?B*CJaJphP=>?A-45DFGHJLNO^_op}~ ῴןןןןןן|qiqhwd?CJaJh'Ohwd?CJaJhwd?B*CJaJphhJyh_CJaJo(hJyh_CJaJhn6h_CJ_H)aJh_CJaJhn6h_CJaJhh_CJaJhKh_CJ_H)aJh_CJ_H)aJhh_CJ_H)aJ!hG6hwd?B*CJ_H)aJph$fgkl=@fg}~ Ѹѧќyh:|CJaJh\f6CJaJhkh\f6CJaJhwd?CJaJh?)hwd?CJaJ h$hwd?CJOJQJ^JaJh$hwd?CJaJo(h$hwd?CJ_H)aJh$hwd?CJaJhwd?B*CJaJphh'Ohwd?CJaJh'Ohwd?CJaJo(/~w x 7 8    $7$8$A$H$gdD $7$8$A$H$gdZF $A$a$gd' $dhA$a$gdZF $7$8$A$H$gd\f6$A$gdM $7$8$A$H$gd:| $7$8$A$H$gdM $A$a$gdM "$&'+,-.UVdfq{  6 : ; @ J Q U Y p x } ~                   ' ( / 7 8    h'CJaJh'h'CJaJhZFCJaJUhkh\f6CJaJh\f6CJaJRbeat classifier using a mixture of experts approach, IEEE Transactions on Biomedical Engineering, 44, 891, 900, 1997. [75] M. G. Tsipouras, D. I. Fotiadis, D. Sideris, Arrhythmia classification using the RR-interval duration signal, The proceeding of the Computers in cardiology Conference, 485-488, 2002. [76] Stephane Mallet, A Wavelet Tour of Signal Processing, Academic Press, 1999. [77] M. R. Homaeinezhad, H. Najjaran Toosi, A. Ghaffari, M. Tahmasebi, M. M. Daevaeiha, Long-Duration Ambulatory Holter ECG QRS Complex Geometrical Templates Extraction by Non-Parametric Clustering of the QRS Virtual Close-up Extracted Feature Space, The Proceedings of the Computing in Cardiology Conference, Belfast, UK, September, 24-27, 2010. [78] Tran Hoai Linh, Stanislaw Osowski, Maciej Stodolski, On-Line Heart Beat Recognition Using Hermite Polynomials and Neuro-Fuzzy Network, On-Line Heart beat recognition using Hermite polynomials and neuro-fuzzy network, IEEE Transactions on Biomedical Engineering, 52, 1224-1231, 2003.     Page:  PAGE \* MERGEFORMAT 1 H(z) G(z) H(z) G(z) H(z) x[n] G(z) H(z) Classification Test & Train Datasets Predicted Labels Feature Extraction Classification Algorithm Feature Space (Dimension=18) Resampling into Frequency 1000 Hz Curve Length Segmentation QRS Close-up Parameters QRS Edges and RR-Tachogram Generating Virtual Image from the Original ECG Discrete Wavelet Transform Reliable QRS Detection-Delineation Algorithm Scale 2 =1,& ,6 Original ECG H*(z) x[n] G(z) H(z) Decomposition ... 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E F 2 2 8 9 - - 3 4   % & e e k l     n n t u   NNTU!EESSTTUUVVWWXXYYZZ[[\\]]^^__``aabbccddeeffgghhiijjkkllmmnnooppqqrrssttuuvvwwxxyyzz{{||}}~~lk5Af;-^{) t8two<; !I a=xS&O"V#`#(5(+}D-^-t1^~1"24416*9M9Q;>3B=C09E`HIK[6PcQQgcST-T\BT+UuW'Y}YY\_]/e3lknuWOSn\fIusanwe^9#QQ VsiO8^8XMa58 ATTAzt $C1j/ sHQHWpmao"t &C:"KoIFH@p@UnknownG* Times New Roman5Symbol3. * ArialA BCambria Math"1hffG G x4??2qHX ?}Y2!xxmrezaehsanCompObjoObjInfoWordDocument[=bSummaryInformation(     Oh+'0l  ( 4 @LT\dmreza Normal.dotmcarlab141Microsoft Office Word@B,@$@BN^[ 0bjbj =bΐΐF `77!!!!!!8 "<G"!>;#;#;#;#;#"$r$,$>>>>>>>?B>>!$$"$$$>77;#;#><<<$ 7 ;#!;#><$><<<;#0#D!)<>>0><B) B<B!<$$<$$$$$>>3@ $$$>$$$$B$$$$$$$$$ : MIT RecTotal # of BeatsRhythm Codes# of Beats of Each Annotated Rhythm# class1st Neural Net2nd Neural Net3rd Neural NetANFIS1002274[43,78,65,86][1,2239,33,1]299.8499.791001001011874[43,78,126,124,81,65][1,1860,4,4,2,3]399.86601001001001022192[43,47,102,78,86][5,2028,56,99,4]599.512699.0499.6199.911032091[43,78,126,65][1,2082,6,2]299.76051001001001042311[43,47,102,126,81,78,86][45,1380,666,37,18,163,2]691.630491.4192.1193.611052691[43,78,86,126,124,81][1,2526,41,88,30,5]597.817797.1697.9598.531062098[126,43,78,86][30,41,1507,520]496.296395.9496.7797.441072140[43,47,86,126][1,2078,59,2]21001001001001081824[43,78,86,120,126,65,124,70,106][1,1740,16,11,41,4,8,2,1]698.804498.4998.9499.511092535[43,76,70,86,126][1,2492,2,38,2]21001001001001112133[43,76,126,86][1,2123,8,1]299.747999.6599.8199.881122550[43,78,126,65][1,2537,10,2]299.807199.7999.81251001131796[43,78,97][1,1789,6]210099.541001001141890[43,78,86,74,70,124,126,65][3,1820,43,2,4,1,7,10]599.767999.5399.8499.941151962[43,78,126,124][1,1953,2,6]299.816999.4699.84299.911162421[43,78,86,65,126][1,2302,109,1,8]399.882499.6699.94521001171539[43,78,126,65][1,1534,3,1]11001001001001182301[43,82,86,65,120,126][1,2166,16,96,10,12]598.455298.1698.631499.451192094[43,78,86,126][103,1543,444,4]499.56497.2899.641001211876[43,78,126,65,86][1,1861,12,1,1]21001001001001222479[43,78,124][1,2476,2]11001001001001231519[43,78,86][1,1515,3]11001001001001241634[43,82,74,86,70,65,126,106][13,1531,29,47,5,2,2,5]695.216094.6795.7496.952002792[43,86,78,65,126,70][148,826,1743,30,43,2]593.441292.4494.1195.842012039[43,78,97,106,86,120,65,74,126,70][35,1625,97,10,198,37,30,1,4,2]891.459892.7192.8693.742022146[43,78,86,65,124,97,70][8,2061,19,36,2,19,1]598.6597.3698.8598.982033107[43,126,78,86,97,124,81,70][45,57,2529,444,2,25,4,1]696.2595.3696.7497.022052672[43,78,86,65,70,126,124][13,2571,71,3,11,2,1]499.5798.2599.4199.912072385[43,82,86,76,91,33,93,126,124,69,65][24,86,105,1457,6,472,6,15,2,105,107]1095.5894.5895.8696.232083040[43,70,86,78,126,124,83,81][53,373,992,1586,24,8,2,2]696.45495.5496.8496.982093052[43,78,65,124,126,86][21,2621,383,7,19,1]597.797.4797.6298.532102685[43,78,86,70,126,97,124,69][17,2423,194,10,17,22,1,1]697.3697.2997.65197.892122763[43,82,78,126,124][1,1825,923,13,1]398.9298.6599.0299.462133294[43,78,70,65,86,97][43,2641,362,25,220,3]594.4692.9795.1295.792142297[43,76,86,126,124,81,34,70][25,2003,256,4,5,2,1,1]599.3898.4599.6399.812153400[43,78,86,126,65,34,70][5,3195,164,30,3,2,1]499.188299.1499.3299.552172280[43,47,102,86,78,126,124][67,1542,260,162,244,4,1]686.57885.6688.1588.872192312[43,78,86,70,34,65,120][21,2082,64,1,4,7,133]697.794197.6197.8797.942202069[43,78,65,126][17,1954,94,4]499.757698.5499.8299.912212462[43,78,86,126][23,2031,396,12]498.865497.7898.9299.312222634[43,78,126,65,106,74][136,2062,15,208,212,1]586.476285.3387.1288.052232643[43,78,86,65,101,70,126,97][28,2029,473,72,16,14,10,1]795.057092.4995.4195.782282141[43,78,124,86,126,65,34][41,1688,24,362,20,3,3]596.475795.5396.8396.982302466[43,78,126,124,86][207,2255,2,1,1]210099.851001002312011[43,82,34,78,120,65,86][11,1254,427,314,2,1,2]497.75009999.1299.892321816[43,82,65,126,106][1,397,1382,35,1]396.270794.8996.5496.742333152[43,86,78,65,70,124][71,831,2230,7,11,2]598.295197.5798.5198.842342764[43,78,126,74,86][3,2700,8,50,3]399.827899.7299.8899.97Total # of Subjects48Average Accuracy (%)98.27375Total # of Complexes112,646     /JTVceqrst    9 : @ C D F K L Q R S h"2hO"5CJ\aJh=xhO"5CJ\_H)aJh=xhO"CJaJhO"5CJH*\aJh=xhO"5CJH*\aJhO"5CJ\aJh=xhO"5CJ\aJ@&:KMSbq$$&`#$/A$Ifa$gd-^Ff$$&`#$/A$Ifa$gd'Y$$&`#$/A$Ifa$gd5A$$&`#$/A$Ifa$gd$C$$&`#$/A$Ifa$gd^-    % FfFf:$$&`#$/A$Ifa$gd-^$$&`#$/Ifa$gd-^% 6 8 @ F L R S W \ k x z Ff\$$&`#$/Ifa$gd-^Ff $$&`#$/A$Ifa$gd-^    % + 1 7 8 < A P a c k q Ff$$&`#$/Ifa$gd-^Ff$$&`#$/A$Ifa$gd-^ ! 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