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Fast multiscale feature fusion for ECG heartbeat classification
EURASIP Journal on Advances in Signal Processing volume 2015, Article number: 46 (2015)
Abstract
Electrocardiogram (ECG) is conducted to monitor the electrical activity of the heart by presenting small amplitude and duration signals; as a result, hidden information present in ECG data is difficult to determine. However, this concealed information can be used to detect abnormalities. In our study, a fast featurefusion method of ECG heartbeat classification based on multilinear subspace learning is proposed. The method consists of four stages. First, baseline and high frequencies are removed to segment heartbeat. Second, as an extension of wavelets, waveletpacket decomposition is conducted to extract features. With waveletpacket decomposition, good time and frequency resolutions can be provided simultaneously. Third, decomposed confidences are arranged as a twoway tensor, in which feature fusion is directly implemented with generalized N dimensional ICA (GNDICA). In this method, corelationship among different data information is considered, and disadvantages of dimensionality are prevented; this method can also be used to reduce computing compared with linear subspacelearning methods (PCA). Finally, support vector machine (SVM) is considered as a classifier in heartbeat classification. In this study, ECG records are obtained from the MITBIT arrhythmia database. Four main heartbeat classes are used to examine the proposed algorithm. Based on the results of five measurements, sensitivity, positive predictivity, accuracy, average accuracy, and ttest, our conclusion is that a GNDICAbased strategy can be used to provide enhanced ECG heartbeat classification. Furthermore, large redundant features are eliminated, and classification time is reduced.
Introduction
Cardiovascular diseases (CVDs) are among the most common causes of death worldwide. Although death rate caused by CVDs has decreased in developed countries, death rate has increased rapidly in developing countries. CVDrelated socioeconomic burden, as well as risk factors, remains astonishingly high [1]. Behavioral risks (e.g., tobacco smoking, physical inactivity, unhealthy diet, etc.), metabolic risks (e.g., raised blood pressure/sugar/lipids), and other risk factors (e.g., gender, advancing age) increase death rates. For instance, cardiac arrhythmia, which refers to disorders of the electrical conduction system of the heart, may pose a high risk and cause medical emergencies.
Electrocardiogram (ECG), as an adjunct tool in cardiovascular diseases management, is used to noninvasively monitor the electrical activity of the heart [2]. To capture frequent occurrence of arrhythmias, medical practitioners record ECG activity for several hours. Large amounts of data are recorded in computational complexity. Therefore, automated heartbeat classification is essential for diagnostic assistance.
Thus far, simple classifiers, such as linear discriminants [3] and Knearest neighbor classifier [4], and complex classifiers, including chaotic modeling, spectral coherence analysis, artificial neural networks, and support vector machine, have been extensively applied. Classifier combination is also used in ECG heartbeat classification to improve accuracy [5]. The final decision regarding classifier combination is achieved by considering the decisions of members or aggregating the decisions of one or a few of the members [2].
Feature extraction is one of the most important steps in classification and can capture a certain underlying property of ECG [6]. Various kinds of comprehensive features have been extracted to describe ECG; these features can be divided into three categories, including temporal, morphological, and statistical features [7]. Temporal features are exclusively acquired from timedomain signals and consist of RR and heartbeat interval features. The hidden complexities of an ECG signal cannot be distinctly interpreted because of subtle changes. More discriminating features can be extracted in a wavelet transform (WT) domain than in a time domain [8]. Morphological and statistical features can be obtained with WT of the ECG signal, which provides good resolution in time and frequency domains [7]. However, WT only displays sufficient frequency resolution at low frequencies but poor frequency resolution at high frequencies. As an extension of WT, waveletpacket decomposition (WPD) is developed to achieve fine frequency resolution at both low and high frequencies. WPD can also be used to investigate piecewise signal variations.
Feature combinations can improve classification results [9]. However, the algorithm of selecting and combining multiple features poses a considerable challenge [3]. In this paper, a novel multiscale featurefusion method for ECG heartbeat classification is proposed. In the proposed method, ECG is initially fragmented into separate heartbeats; baseline and noise are further removed from each heartbeat. Features are then extracted by waveletpacket decomposition, in which features become more distinguishable in a waveletpackettransform domain than in a time domain. The fourthlevel components of WPD are represented as the features of a heartbeat. All of the features of a heartbeat are arranged into a twoorder tensor rather than a long vector; a twoorder tensor is further processed by generalized N dimension independent component analysis (GNDICA) to select and fuse effective components simultaneously. These fused components, as new features of ECG heartbeats, are fed to a support vector machine (SVM) for automated classification. Simulation results from an MITBIH arrhythmia database demonstrate high average accuracies of 98, 98.79, 98.87, and 99.43 % of detected normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), and fusing normal and ventricular ectopic beats (F), respectively. The proposed method is compared with five currently used methods and two conventional fusion methods by using the MITBIH database. The proposed method can improve ECG classification results compared with other methods.
Methodology
The proposed ECG heartbeat classification method is divided into four parts, namely preprocessing, feature extraction, feature fusion, and classification. Our main contribution is found in the third section, that is, feature fusion, which involves a training stage and a test stage. Each stage is briefly described in Fig. 1.

(1)
In the preprocessing stage, baseline and highfrequency noises are initially removed from the original input ECG signals. Heartbeats are then segmented on the detected real R points.

(2)
In the feature extraction stage, waveletpacket decomposition is used to extract WP coefficients as heartbeat features. Fourthlevel decompositions are used as features in the following procedure.

(3)
In the featurefusion stage, all of the extracted features of an ECG heartbeat are arranged as a twoway tensor, in which featurefusion procedure is implemented using a multilinear subspacelearning method, GNDICA.

(4)
In the classification stage, SVM is directly used as a classifier in finalheartbeat classification.
Preprocessing
ECG is composed of atrial depolarization (P wave), ventricular depolarization (QRS complex), and ventricular repolarization (T wave). These waves are induced by specific electrical phenomena on the cardiac surface. ECG contains diverse kinds of noises, such as baseline wander, powerline interference, and highfrequency noise. Baseline wander caused by respiration or patient movement can be corrected by subtracting the filtered signal from the original signal with two median filters. These two median filters with widths of 200 and 600ms are used to remove ORS complexes, Pwaves, and Twaves, respectively [10]. After the baseline of ECG is corrected, powerline interference and highfrequency noise are removed using a lowpass filter. Figure 2a, b show a part of the original signal and the signal in which the noise and baseline are removed, respectively.
Considering that this study does not contribute to heartbeat detection in ECG, we directly segment the filtered ECG signals by using heartbeat fiducialpoint times provided in the MITBIH arrhythmia database. Fiducial points (R points) occur instantaneously in the major local extreme of a QRS complex. However, several detected Rpoint locations are skipped using this provided annotation (Fig. 3a). A time window with a length of 40 samples near the detected R points is utilized to determine real R point locations, where the highest value is found inside the windows (Fig. 3b). Based on the real R points, 99 samples before the R points and 100 samples after the R points are used as the real heartbeat samples, (Fig. 3c).
Feature extraction
This study mainly investigates the process by which heartbeat features are extracted. Preprocessed ECG heartbeats are utilized in all of the following processing methods. Wavelets provide relevant information to extract features. A multiresolution representation of nonstationary ECG can be obtained by wavelet analysis, which provides a levelbylevel transformation of a signal from a time domain to a frequency domain. In waveletpacket analysis (WPD) according to wavelet theory, a normalized signal is transmitted through a series of low and highpass filters simultaneously [11, 12]; time domain is then transformed into frequency domain in each level. Unlike wavelet transformation, WPD divides the frequency subband of a signal with an equal width by using sets of orthonormal basis functions. A signal is split into approximation (A) and detail (D) coefficients; these coefficients are further split into nextlevel A and D coefficients. Afterward, the process is repeated. Figure 4 illustrates waveletpacket analysis of levelfour decomposition. The top level in Fig. 4 is the time representation of the ECG signal. Time and frequency resolutions are traded off in each level. The bottom level shows the frequency representation of a signal, which decomposes both the approximation and details coefficients.
After waveletpacket analysis of a heartbeat is conducted, a rich collection of abundant information with arbitrary timefrequency resolution is obtained. This information shows that nonstationary and stationary characteristics of the extracted features are combined.
The fourlevel decomposition of input ECG signal is provided by WPD, which transfers the time domain to the frequency domain (Fig. 5). With the levels computed from top to bottom, time resolution decreases, whereas frequency resolution increases. After the original signal in the top level is decomposed (Fig. 5a), the data in the next level of nodes (D1) show lowscale and highfrequency properties. Approximations in each level of A1, AA2, AAA3, and AAAA4 nodes appears similar to the original approximations. In our study, a fourlevel WPD is used to obtain the features of an ECG signal.
Feature fusion
After extracting wavelet coefficients in the fourthlevel decomposition of an ECG signal, we aim to fuse all of the features to effectively classify heartbeats. A concept of tensorbased multilinear subspacelearning method called generalized N dimensional independent component analysis (GNDICA) is introduced to perform multiplefeature fusion. It can effectively combine and select all of the features extracted from the original signal and considers the interrelationship among different waveletpacket coefficients. A concept of “tensor” is introduced to arrange all of the features of one heartbeat. A tensor is an Nway array [13], and the order of a tensor is known as mode [14].
In previous studies [15, 16], each mode of a tensor should be defined distinctively. Therefore, the ECG heartbeat can be represented using a twoway tensor \( {X}_i^{tr}\in {R}^{I_1\times {I}_2},i=1,2,\dots, n \) by adjoining the above extracted features. In this tensor, mode1 is denoted by the number of the bottom level of a full waveletpacket decomposition (I _{1}D), and mode2 is represented by the number of features (I _{2}D). This twoway tensor is presented in Fig. 6. In this manner, the factor inherent to the element of feature vectors and the factor among different features can be disentangled. An optimal demixing matrix on each mode is determined by GNDICA with a series of training tensor samples [17].
Given a series of training twoway tensors \( {X}_i^{tr}\in {R}^{I_1\times {I}_2},i=1,2,\dots, n \) by learning two demixing matrices \( {\mathbf{W}}_z\in {R}^{J_z\times {I}_z},z=1,2\left({J}_z\le {I}_z\right) \) , GNDICA finds another set of lowerrank core tensor \( {S}_i^{tr}\in {R}^{J_1\times {J}_2},i=1,2,\dots, n \) , that retains as much of the variation of the original data as possible and in which the elements on each mode are as independent as possible:
The optimization problem of GNDICA is defined to minimize error function, as expressed in the following equation [9, 10, 18]:
where A _{ z }, z = 1, 2 is the pseudoinverse of the demixing matrix W _{ z }, z = 1, 2.
To effectively represent a heartbeat signal, we fuse all of the features by demixing matrices W _{ z }, z = 1, 2 in each mode of feature representation \( {X}^{te}\in {R}^{I_1\times {I}_2} \). In this process, the factor inherent to the element of feature vectors and the factor among different features are considered. Thus, we obtain the following expression:
which is used in heartbeat classification.
Classification
After distinguishing features are extracted from each heartbeat, a classifier is applied to provide the ECG heartbeat classification. In this paper, SVM [19], as an effective tool used to solve numerous classification problems, is used in heartbeat classification.
In our study, LIBSVM package is used. For fair comparison, the optimum parameters of the kernel function (radial basis function) are assigned to SVM for each signal representation [20].
Experimental results
The MITBIH arrhythmia database is utilized in our study [21, 22]. Approximately 109,000 heartbeats contained in 48 ECG recordings can be achieved for approximately 30 min in each recording. MITBIT heartbeat types are classified into five main classes according to the standards recommended by the Association for the Advancement of Medical Instrumentation. Heartbeats belonging to class N and originating from the sinus node are normal and bundlebranchblock beat types. Supraventricular ectopic beats and ventricular ectopic beats belong to classes S and V, respectively. Fused normal and ventricular ectopic beats belong to class F. The unknown heartbeats, including paced beats, are classified in class Q. The ECG signals of the MITBIH arrhythmia database are sampled at 360Hz, and 200 sampling points are used for signal representation.
All of the experiments are conducted using a standard PC (Intel (R) Core (TM) i74770 CPU @ 3.40GHz), and the learning phase to obtain the transformation matrix is carried out in Matlab 2013 [23]. The performance of the proposed featurefusion method is evaluated using all of the heartbeat segments found in the MITBIT database. In this study, random subsampling is conducted to train and evaluate classifiers. Data are randomly selected from the whole database ten times; the number of each class is shown in Table 1. The schematic of average heartbeats in each class of training heartbeats is shown in Fig. 7.
WPD was computed in each of the ECG heartbeat by using discrete approximation of the Meyer wavelet, which is considered as the most efficient decomposition of feature extraction [24]. All of the decomposition coefficients in the fourth level are used as features, with 16 sets of vectors and 107 components. The performance of the multilinear subspacelearning method GNDICA and the linear subspacelearning method PCA of heartbeat classification is described in terms of sensitivity, positive predictivity, accuracy, and average accuracy.
Evaluation criterion
Four classes, namely, N, S, V, and F, are used in the experiments. We assume that i, j ∈ {N, S, V, F}, C _{ i,j } is the number of heartbeats of class i classified as j. If ∀ i ≠ j, then C _{ i,j } is an incorrectly classified heartbeat, whereas C _{ i,i } is a correctly classified heartbeat [7]. We define B _{ j } = ∑_{∀ j } C _{ i,j } as the total number of examples originally belonging to class i; A _{ j } = ∑_{∀ i } C _{ i,j } as the total number of examples labeled as class j; and C _{ total } = ∑_{∀ i ∀ j } C _{ i,j }. We denote TP _{ i } = C _{ i,i } as the true positives of I, TN _{ i } = C _{ total } − B _{ i } − A _{ i } + C _{ i,i } as the true negatives of I, FP _{ i } = A _{ i } − C _{ i,i } as the false positives of I, and FN _{ i } = B _{ i } − C _{ i,i } as the true negatives of i. The accuracy (AC _{ i }) of i is the proportion of the total number of correct predictions defined as follows:
The sensitivity (Sens _{ i }) of i refers to the ability of the method to correctly identify the heartbeat with the corresponding class.
The positive predictive value (PPV _{ i }) of i is a proportion used to determine the probability that the result actually belongs to a particular class if the result is positive:
The average accuracy (MAC _{ total }) is defined as Eq. 22 corresponding to the average classification rate of all classes:
Classification performance of different features
After WPD, PCA, and GNDICA are conducted, the features are fed to SVM for automated classification [25]. The average classification rates of the methods except WPD vary with the dimension of reduced features. Figure 8 shows the average accuracy in each experiment by using PCA components and GNDICA components, respectively. In this study, the original dimension of WPD features (107 × 16 = 1712) reduced into 10, 12, 14, 16, 18, and 20 dimensions is investigated. We observe that GNDICA can improve the average accuracy, although the dimension is reduced to ten. In addition, the highestaverage accuracy is obtained by GNDICA with 16 dimensions. Therefore, GNDICA not only reduces high dimension of original concatenation features but also improves classification results.
Average accuracies (Fig. 8) are obtained with the optimum parameter gamma in the radial basis function of SVM. For example, numerous possible values in SVM of GND_ICA_16 are tested, and MAC _{ total } is plotted with respect to different gamma values (Fig. 9). The highestaverage accuracy is provided at gamma = 0.7, and the corresponding average accuracy is 97.54 % (Fig. 9).
Classification Evaluation with Different Criteria
Considering that the average accuracy is changed according to dimension reduction, we select the highestclassification rate to compare our results.
Table 2 displays the complete classification description obtained by applying conventional featurefusion methods or tensorbased featurefusion methods in the form of confusion matrices. Performance detail per beat is provided by these matrices. Considering that the average accuracy is changed according to dimension reduction, we obtain all of the results from the best experimental set with 16 dimensions to achieve unity and compare the results. Table 3 shows the results obtained using the different methods on the basis of the introduced evaluation criteria. In conventional methods, all of the features should be concatenated as a vector (107 × 16 = 1712) and then projected into a PCA subspace. In the tensorbased method, all of the features should be arranged as a twoway tensor (size of 107 × 16), and directly processed by GNDICA.
Differences between proposed method and conventional methods should be quantified. A ttest is a statistical test that can be used to determine if two sets of groups are significantly different from each other. A p value is a parameter of ttest used to assess significant difference. In general, a p value equal to or less than 0.05 is regarded as a significant difference and less than 0.01 is considered a highly significant difference. In addition, smaller p value corresponds to greater significant difference between two groups. Table 4 shows the p value obtained from algorithmlabeled class (GNDICA) and conventionallabeled classes (WPD, PCA). After the difference between GNDICA and WPD is evaluated, a highly significant difference is found between S and V. After the difference between GNDICA and PCA is evaluated, a highly significant difference is found between S and V; likewise, a significant difference is observed in N. Therefore, highly significant differences are observed between GNDICA and WPD and between GNDICA and PCA. This result indicates that the average accuracy of the proposed method is only less than 2 % higher than that of WPD or PCA. However, highly significant differences can be achieved in ECG heartbeats classification.
Computational efficiency
Computational efficiency is investigated using the computing time of the transformation matrix of the methods with 16 dimensions. In the multilinear subspacelearning methods, tensor mode 1 is reduced to 16D, and mode 2 is reduced to 1D. In all of the training heartbeats, GNDICA requires 9.73 s to calculate the transformation matrices. This time is considerably shorter than 19.59 s, which is required by PCA.
Comparison with stateoftheart methods
The MITBIH arrhythmia database is the standard database used in current methods to evaluate performance. Therefore, a comprehensive summary of ECG heartbeat classifications is provided using this database (Table 5). In [26], temporal features (preRRinterval, postRRintervals, average RRintervals, and local average RRinterval), and morphological features (STbased, WTbased, and combinations) are extracted, and five classes (N, S, V, F, Q) are classified using a multilayer perceptron neural network classifier with an average accuracy of 97.5 %. Wavelettransformed ECG waves with timing information are the feature of the two classes (normal and premature ventricular contractions) in [27]. The method achieves 95.16 % accuracy with neural network as a classifier. Hermite transform coefficients and the time interval between two neighboring Rpeaks are used in [28] as features and input to the blockbased neural networks for heartbeats classification, and the average accuracy is 96.6 %. Morphologicalwavelet transform features reduced by PCA and temporal features are extracted in [29] at 95.58 % accuracy. A classifier is composed of feedforward and fully connected artificial neural networks, which are improved by a multidimensional particle swarm optimization technique. Five types of heartbeats are classified in [30] using bispectrum features further reduced by PCA for dimension reduction. An average accuracy of 93.47 % is achieved using a leastsquaresupport vector machine with a radial basis function.
Conclusions and discussion
ECG heartbeat classification is one of the most significant research fields in computeraided diagnosis. A study of a featurefusion method based on a multilearning subspacelearning algorithm called GNDICA for ECG heartbeat classification is proposed. The commonly used MITBIH arrhythmia database is employed in all of our experiments. ECG signals are segmented after baseline; highfrequency noise is removed and fiducial points are detected. Four groups labeled in the MITBIH arrhythmia database are selected and used in our classification study. These labeled ECG heartbeats include normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), and fused normal and ventricular ectopic beats (F). Waveletpacket decomposition, as a technique used to analyze the relationship between time and frequency information is also performed to extract features. Waveletpacket coefficients extracted in the fourth level, which is composed of approximations and details, are used for further feature fusion. A total of 16 sets of coefficients with a size of 107 represent one ECG heartbeat simultaneously. In contrast to linear subspacelearning methods (PCA) in which all of the features should be transformed as a vector, multilinear subspacelearning method (GNDICA) can be used to process input data in a tensor form. Thus, 16 sets of coefficients features are further fused with GNDICA by arranging them as a twoway tensor; in this technique, the factor inherent in the element of feature vectors and the factor among different features are considered. With SVM, the fused features are used to discriminate four different types of heartbeats. Five common evaluation criteria, including sensitivity, positive predictivity, accuracy, average accuracy, and ttest, are used to investigate classification performance. Based on the classification results, our conclusion is that performance of GNDICAbased feature fusion is more distinguished than that of the linear subspacelearning method PCA. Furthermore, the computing time of the transformation matrices of GNDICA is considerably shorter than that of conventional PCA. Thus, GNDICA not only improves the classification time but also eliminates numerous redundant features, prevents the drawbacks of dimensionality.
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Acknowledgements
This work was supported by the National Basic Research Program of China (2013CB328806), the Key Projects in the National Science & Technology Pillar Program (2012BAI02B01), China Postdoctoral Science Foundation funded project (2014M560050).
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Ai, D., Yang, J., Wang, Z. et al. Fast multiscale feature fusion for ECG heartbeat classification. EURASIP J. Adv. Signal Process. 2015, 46 (2015). https://doi.org/10.1186/s1363401502310
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Keywords
 ECG heartbeat
 Classification
 Multilinear subspace learning
 Waveletpacket decomposition