An arrhythmia classification method based on selected features of heart rate
`variability signal and support vector machine-based classifier
`
`F. Yaghouby, A. Ayatollahi
`
`Department of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran
`Farid.yaghouby@gmail.com
`
`Abstract
` In this paper we present an arrhythmia classifi-
`cation method using Heart Rate Variability (HRV) signal
`features and Support Vector Machine (SVM) Classifier. Eight
`linear and nonlinear features are extracted from the HRV
`signals and a subset of these features is selected using the Im-
`proved Forward Floating Selection (IFFS) method to train the
`classifier. BSVM is a classification algorithm based on SVM
`which is able to solve the multi-class classification problems.
`here, five types of the most life threatening cardiac arrhyth-
`mias including normal sinus rhythm, atrial fibrillation, ventri-
`cular fibrillation, ventricular bigeminy and sick sinus syn-
`drome can be discriminated by BSVM and selected features
`with the average accuracy of 99.78%.
`
`Keywords Heart rate variability signal, Arrhythmia classifi-
`cation, Support vector machine, Forward floating
`feature selection
`
`I. INTRODUCTION
`
`Nowadays the cardiac arrhythmias are the most famous
`causes of mortality. Hence, several techniques have been
`proposed to identify and detect the different types of arr-
`hythmia. These techniques usually extract desired features
`from ECG or HRV arrhythmic signals to classify them.
`Since ECG signal processing is time consuming and too
`sensitive to the amount of the noise, many researchers ana-
`lyze HRV signals to detect abnormal rhythms. Some exam-
`ples of these automatic arrhythmia detection and classifica-
`tion techniques
`are neural networks
`[1,2], wavelet
`transforms [3], support vector machines [4], fuzzy logic [5]
`and the rule-based algorithms [6].
`The proposed algorithm in this paper presents an HRV-
`based arrhythmia classification method which can detect
`and classify five types of the most famous abnormal cardiac
`rhythms. These arrhythmias are namely the Normal Sinus
`Rhythm (NSR), the Atrial Fibrillation (AF), the Ventricular
`Fibrillation (VF), the Ventricular Bigeminy (B) and the Sick
`Sinus Syndrome (SSS). This technique is based on the IFFS
`feature selection method and SVM-based classifier. In the
`first step, IFFS selects the optimal subset of features from
`the 8 original features and then SVM separates the arrhyth-
`mia classes in the selected feature space. IFFS finds the best
`subset of features by evaluating a criterion function [7].
`It
`
`not only reduces the cost of feature extraction methods like
`Principle Component Analysis (PCA), but also improves the
`classification accuracy.
`SVM, first proposed by Vapnik in 1998 [8], has been used
`as a powerful tool for classification problems. Here, we
`propose the multi-class BSVM formulation [9] for arrhyth-
`mia classification. As it reported in the past, we can see that
`SVM provides more accurate results in classification than
`other methods such as the neural networks.
`The details of the mentioned method for arrhythmia classi-
`fication using HRV features are presented in continue. In
`the first section, we explain the steps extracting ECG sig-
`nals and preprocessing it in order to obtain HRV signals.
`Afterwards, a range of linear and nonlinear features are
`extracted from HRVs and then by using the IFFS the di-
`mensionality of these original features are reduced to 4.
`Finally, in the last section the BSVM multi-class classifier
`is applied to the selected features to detect any types of 5
`cardiac arrhythmias.
`
`II. MATERIALS AND METHODS
`
`A. Extracting and preprocessing the signal
`
`The MIT-BIH arrhythmia data base is a standard reference
`for ECG signal processing which includes 48 ECG record-
`ings each with a length of 30 min [10]. All signals in this
`database were filtered in the frequency range of 0.1-100Hz
`and were sampled with a sampling rate of 360Hz. We ex-
`tract the ECGs relating to NSR, AF, B and SSS arrhythmias
`from this database. In addition to this database, we use the
`Creighton University Ventricular Tachyarrhythmia Data-
`base to obtain the VF signals after resampling it at a rate of
`360 Hz. As the first step it is necessary to extract HRV
`signals from these ECG signals. For this purpose the inter-
`fering signals are eliminated using a 5-15Hz bandpass filter.
`Then the wave R in the filtered signals is detected by using
`Hamilton and Tompkins algorithm [11]. For constructing
`the HRV signal we first measure the time intervals between
`the successive waves R in each ECG signal and then plot
`this intervals against the time indices. The obtained HRV
`signals are divided into the same length segments each
`
`O. Dössel and W.C. Schlegel (Eds.): WC 2009, IFMBE Proceedings 25/IV, pp. 1928–1931, 2009.
`www.springerlink.com
`
`APPLE 1040
`
`1
`
`

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`An Arrhythmia Classification Method Based on Selected Features of Heart Rate Variability Signal
`
`1929
`
`ApEn: The Approximate Entropy shows the unpredictability
`of the fluctuations in a time series. Large values of ApEn
`show high irregularity and smaller values of it indicate more
`regular time series. The proposed method in [15] is used to
`calculate ApEn for each segment of HRV signal.
`Now, we have 8 features for each of 889 HRV segments.
`These all features are normalized within the range of [0, 1]
`initially. We then simplify the proposed method reducing
`the number of features. For this purpose a new feature se-
`lection method named Improved Forward Floating Selection
`(IFFS) is used to select an optimal subset of original fea-
`tures. This algorithm has a new search strategy to check
`whether removing any feature in the selected feature set and
`adding a new one at each sequential step can minimize the
`criterion function (misclassification rate). The results show
`that this method compared with other techniques selects the
`optimal subset of features and requires significantly less
`computational time.
`By applying the IFFS to the original feature space, 4 fea-
`tures are selected from 8. These selected features are Mean
`RR, RMSSD, pNN50 and D2.
`
`Fig. 1 Box-plots of the four selected features for different arrhythmia
`classes (1 = NSR, 2 = AF, 3 = VF, 4 = B, 5 = SSS).The values are norma-
`lized between 0 and 1.
`
`The box-plots of the four selected features for different
`arrhythmia classes are presented in Fig. 1. As seen each of
`the selected features has a value in a range that differs from
`one class to another. In fact we can say by using these fea-
`tures we have better discrimination between the 5 classes.
`
`containing 64 R-R intervals. Totally, we have 889 HRV
`segments including 341 NSR segments, 340 AF segments,
`142 VF segments, 37 B segments and 24 SSS segments.
`
`B. Extracting features and selecting optimal subset
`
`To illustrate linear and nonlinear behavior of cardiovas-
`cular system, it is necessary to consider both linear and
`nonlinear features of cardiac signals. So we consider a com-
`bination of linear and nonlinear features. The linear features
`which are obtained from time and frequency domains are
`calculated based on the proposed standard in [12]. The li-
`near features are 5 and include:
`(cid:120) Time domain features: This features which are ex-
`tracted from the R-R interval time series directly are:
`Mean RR: The mean value of the 64 R-R intervals in
`each segment.
`STD RR: The standard deviation of the 64 R-R inter-
`vals in each segment
`RMSSD: The root mean square successive difference
`of the 64 R-R intervals in each segment
`pNN50: The number of successive difference of 64
`R-R intervals that differs more than 50 ms, respec-
`tively, divided by 64.
`(cid:120) Frequency domain features: These features are ex-
`tracted to discriminate between sympathetic and para-
`sympathetic contests of the HRV signals. In this work
`we calculate Power Spectral Density (PSD) for the
`High Frequency (HF) band (0.15-0.4Hz) and Low Fre-
`quency (LF) band (0.04-0.15Hz) and the ratio of the LF
`and HF bands power (LF/HF) as the Frequency domain
`feature of the HRV signal.
`On the other hand, HRV signal analysis by help of me-
`thods on nonlinear dynamics leads to very valuable informa-
`tion for physiological interpretation of the heart. So we extract
`these 3 nonlinear features:
`LLE: The Largest Lyapunov Exponent provides useful
`information about the dependency of system on initial con-
`ditions and a positive lyapunov exponent confirms the exis-
`tence of chaos in the system. For calculating LLE a point is
`selected in the reconstructed phase space of the system and
`all neighbor points residing within a predefined radius are
`determined. As the system evolves,
`the mean distances
`between the trajectory of the initial point and the trajectories
`of the neighbor points are calculated. Then the logarithm of
`these mean values plots against the time and the slope of the
`resulting line is considered as LLE [13].
`D2: The Correlation Dimension is a measure of complex-
`ity of the time series and determines the minimum number
`of dynamic variables which can model the system. We use
`the algorithm presented in [14] in order to estimate this
`feature.
`
`IFMBE Proceedings Vol. 25
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`1930
`
`F. Yaghouby and A. Ayatollahi
`
`Table 1 The Confusion Matrix on the test set. The values are average of 100 train and test procedures
`
`Total number of
`train/test segments
`223.07/117.93
`221.57/118.43
`90.01/51.99
`22.98/14.02
`14.37/9.63
`
`Database
`annotation
`
`NSR
`AF
`VF
`B
`SSS
`
`
`C. Classification based on SVM
`The last step of the proposed algorithm is classification
`of arrhythmias using the selected features. As it mentioned
`we use SVM as a classifier here.SVM is a machine-learning
`technique which identifies the best separating hyper plane
`between the two classes [16]. Although SVM can separate
`the input data into only two classes, the multi-class classifi-
`cation is also possible by BSVM formulation. Suppose the
`training vectors are:
`
`(1)
`
`The aim of BSVM is training the following classification
`
`where
`
`(2)
`
`The parameters of the rule above are determined by solv-
`ing the BSVM formulation:
`
`(cid:4668)(cid:4666)(cid:1876) (cid:1877)(cid:4667)(cid:485)(cid:4666)(cid:1876)(cid:1864)(cid:1877)(cid:1864)(cid:4667)(cid:4669)
`(cid:2286)
`where(cid:1488)(cid:2290)(cid:1603)(cid:2284)
`(cid:1861)(cid:1488)(cid:2291) (cid:4668)
`(cid:485)(cid:1855)(cid:4669)
`rule(cid:1869)(cid:4666)(cid:1876)(cid:4667)
`(cid:1853)(cid:1870)(cid:1859)
`(cid:1877)(cid:1488)(cid:2291)(cid:1858)(cid:1877)(cid:1876)
`(cid:1731)(cid:2009)(cid:1877)(cid:1863)(cid:1845)(cid:1876)(cid:1732)
` (cid:1858)(cid:1877)(cid:4666)(cid:1876)(cid:4667)
`(cid:1854)(cid:1877) (cid:1877)(cid:1488)(cid:2291)
`(cid:1875)(cid:1854) (cid:963)
`(cid:4666)(cid:1849)(cid:1499)(cid:1854)(cid:1499)(cid:2022)(cid:1499)(cid:4667)
`(cid:3630)(cid:2033)(cid:1877)(cid:3630)
`(cid:1854)(cid:1877)
`(cid:1853)(cid:1870)(cid:1859)
`(cid:1829)(cid:963) (cid:963)
`(cid:2022)(cid:1861) (cid:1868)
`(cid:1877)(cid:1488)(cid:1851)
`(cid:1877)(cid:1488)(cid:1851)(cid:4668)(cid:1877)(cid:4669)
`(cid:1861)(cid:1488)(cid:1835)
`(cid:1854)(cid:1877)(cid:1861)(cid:3398)(cid:3435)(cid:1731)(cid:2033)(cid:1877)(cid:1876)(cid:1861)(cid:1732)
`(cid:1731)(cid:2033)(cid:1877)(cid:1861)(cid:1876)(cid:1861)(cid:1732)
`(cid:1854)(cid:1877)(cid:3439)(cid:3410) (cid:3398)(cid:588)(cid:1861)(cid:1877) (cid:1861)(cid:1488)(cid:1835)(cid:1488)(cid:1851)(cid:4668)(cid:1877)(cid:1861)(cid:4669)
`(cid:588)(cid:1861)(cid:1877)(cid:3410) (cid:1861)(cid:1488)(cid:1835)(cid:1877)(cid:1488)(cid:1851)(cid:4668)(cid:1877)(cid:1861)(cid:4669)
`
`subject to
`
`(3)
`
`the Mitchell-Demyanov-
`use
`p=2, we
`Considering
`Malozemov algorithm [17] to solve (3). Furthermore, we
`select the Radial Basis Function (RBF) as the kernel in (2).
`So, we have two free parameters that must be assigned
`correctly. The first is the width of RBF kernel and the
`second is the Regularization parameter in (3). We select
`(cid:305)(cid:32)(cid:19)(cid:17)(cid:21)(cid:3)(cid:68)(cid:81)(cid:71)(cid:3)(cid:38)(cid:32)10 empirically.
`
`NSR
`117.93
`0.22
`0
`0.06
`0
`
`classification
`VF
`0
`0.03
`51.98
`0.01
`0
`
`AF
`0
`117.55
`0.01
`0.77
`0
`
`B
`0
`0.63
`0
`13.18
`0
`
`SSS
`0
`0
`0
`0
`9.63
`
`III. RESULTS
`
`Finally, the all 889 segments of different arrhythmias are
`randomly divided to train and test sets in an approximate
`ratio of 2/3 and 1/3. After training the SVM classifier we
`use test set to evaluate the classification performance. This
`procedure is repeated 100 times and each time we divide
`whole data into train and test sets and carry out classifica-
`tion using these sets. The average Confusion Matrix ob-
`tained from 100 different test sets are presented in Table 1.
`As seen for the NSR and SSS we have no misclassification
`to other classes (0%). for the AF in average only 0.88 seg-
`ments are also misclassified (0.007%), for the VF in aver-
`
`age 0.01 segments are misclassified ((cid:1542)0%) and for B in
`
`average 0.84 segments are misclassified (0.06%).
`In continue the four famous measures sensitivity, speci-
`ficity, positive predictivity and accuracy are derived from
`the proposed algorithm. Furthermore, to compare the effi-
`ciency of the proposed method, these parameters are calcu-
`lated for the SVM classifier which is trained using the 8
`original features too. Table 2 shows the average values of
`these parameters for both mentioned algorithm. As we can
`see the presented method can discriminate the NSR with an
`average accuracy of 99.91%, the AF with 99.47%, the VF
`with 99.98%, the B with 99.53% and SSS with 100%. These
`results demonstrate the effectiveness of this method in the
`classification of cardiac arrhythmias. As a comparative
`study we can see the results of the classification using origi-
`nal features too. Table 2 shows that the classification using
`the 4 selected features has better results than the classifica-
`tion using the 8 features. The average values of the perfor-
`mance parameters shows that when we use the selected
`features instead of the original features for classification we
`have an increment about 2% in the sensitivity, 0.2% in the
`specificity, 0.5% in positive predictivity and 0.2% in the
`accuracy. The use of selected features on the other hand
`decreases the SVM training time significantly. So the pro-
`posed classification algorithm based on IFFS and SVM
`classifier, not only decrease the processing time but also
`makes a noticeable increase in accuracy of classification.
`
`IFMBE Proceedings Vol. 25
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`3
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`

`

`An Arrhythmia Classification Method Based on Selected Features of Heart Rate Variability Signal
`
`1931
`
`Table 2 Performance analysis of the BSVM classifier on the original features and the selected features in terms of the average values of the four commonly
`used measures in %.( number inside the parenthesis are the standard deviations)
`
`Classification using 4 selected features
`
`Classification using 8 original features
`
`Arrhythmia
`classes
`
`Sensitivity
`(%)
`
`Specificity
`(%)
`
`NSR
`AF
`VF
`B
`SSS
`
`100
`99.26
`99.98
`94
`100
`
`99.85
`99.57
`99.98
`99.79
`100
`
`Positive
`Predictivity
`(%)
`
`99.76
`99.34
`99.92
`95.44
`100
`
`Accuracy
`(%)
`
`Sensitivity
`(%)
`
`Specificity
`(%)
`
`Positive
`Predictivity
`(%)
`
`Accuracy
`(%)
`
`99.91
`99.47
`99.98
`99.53
`100
`
`100
`99.14
`99.42
`89.28
`93.86
`
`99.39
`99.2
`99.95
`99.68
`100
`
`99.03
`98.68
`99.72
`94.32
`100
`
`99.62
`99.25
`99.83
`99.38
`99.8
`
`Average
`
`98.65(2.61)
`
`99.84(0.16)
`
`98.89(1.95)
`
`99.78(0.26)
`
`96.38(4.68)
`
`99.68(0.36)
`
`98.35(1.02)
`
`99.58(0.25)
`
`IV. CONCLUSIONS
`
`In this paper an effective HRV-based arrhythmia classifi-
`cation method has been presented. We first extract 8 fea-
`tures from HRV segments and then in order to reduce the
`learning time and also to improve the efficiency of the clas-
`sifier, 4 optimal features are selected from 8 original fea-
`tures using the IFFS algorithm. Then a SVM-based multi-
`class classifier method named BSVM is used to classify the
`5 types of arrhythmias. Comparing the results that have
`been shown in Table 2 we find that the proposed technique
`outperforms the same classifier which is applied to the orig-
`inal
`features producing the classification accuracy of
`99.91%, 99.47%, 99.98%, 99.53% and 100% for the arr-
`hythmia classes of NSR, AF, VF, B and SSS respectively.
`
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