Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal
Introduction
Heart diseases are a major cause of mortality in the developed countries. Many different instruments and methods are developed and being daily used to analyze the heart behavior. One of the relatively new methods to assess the heart activity and to discriminate different cardiac abnormalities is to analyse the so-called heart rate variability (HRV) signal. HRV signal, which is generated from electrocardiogram (ECG) by calculating the inter-beat intervals, is a nonlinear and nonstationary signal that represents the autonomic activity of the nervous system and the way it influences the cardiovascular system. Hence, measurement and analysis of the heart rate variations is a non-invasive tool for assessing both the autonomic nervous system and the cardiovascular autonomic regulatory system. Furthermore, it can provide useful information about the current and/or the future heart deficiencies [1]. Therefore, HRV analysis can be considered as an important diagnostic tool in cardiology.
Several methods have been proposed in the literature for automatic cardiac arrhythmia detection and classification. Some examples of the techniques used include: threshold-crossing intervals [2], neural networks [3], [4], [5], [6], [7], [8], [9], [10], wavelet transforms [11], wavelet analysis combined with radial basis function neural networks [12], support vector machines [13], Bayesian classifiers [14], fuzzy logic combined with the Markov models [15], fuzzy equivalence relations [16], and the rule-based algorithms [17]. Most of these studies [2], [3], [4], [5], [6], [11], [12], [13] are based on the analysis of the ECG signal itself. In most methods, the various features of the ECG signal including the morphological features are extracted and used for classification of the cardiac arrhythmias. This is a time-consuming procedure and the results are very sensitive to the amount of the noise.
An alternative approach would be to extract the HRV signal from the ECG signal first by recording the R–R time intervals and then processing the HRV signal instead. This is a more robust method since the R–R time intervals are less affected by the noise. Different HRV signal analysis methods for cardiac arrhythmia detection and classification were introduced in the past. Tsipouras and Fotiadis [8] proposed an algorithm based on both time and time–frequency analysis of the HRV signal using a set of neural networks. Their method could only classify the input ECG segments as “normal” or “arrhythmic” segments without the ability to identify the type of the arrhythmia. Acharya et al. [16] employed a multilayer perceptron (MLP) together with a fuzzy classifier for arrhythmia classification using HRV signal. They could classify the input ECG segments into one of the four different arrhythmia classes. In [17], Tsipouras et al. proposed a knowledge-based method for arrhythmia classification into four different categories. The main drawback of their algorithm was the fact that the atrial fibrillation, which is an important life-threatening arrhythmia, was excluded from the ECG database.
In this paper a new arrhythmia classification algorithm is proposed which is able to effectively identify six different and more frequently occurring types of cardiac arrhythmia. These arrhythmias are namely the normal sinus rhythm (NSR), the premature ventricular contraction (PVC), the atrial fibrillation (AF), the sick sinus syndrome (SSS), the ventricular fibrillation (VF) and the 2° heart block (BII). The proposed algorithm is based on the two kernel learning machines of the generalized discriminant analysis (GDA) and the support vector machine (SVM). By cascading SVM with GDA, the input features will be nonlinearly mapped twice by radial basis function (RBF). As a result, a linear optimal separating hyperplane can be found with the largest margin of separation between each pair of arrhythmia classes in the implicit dot product feature space.
GDA is a data transformation technique which was first introduced by Baudat and Anouar [18]. It can be considered as a kind of generalization to the well-known linear discriminant analysis (LDA) algorithm and has become a promising feature extraction scheme [19], [20], [21], [22], [23], [24] in recent years. The main steps in GDA are to map the input data into a convenient higher dimensional feature space F first and then to perform the LDA algorithm on the F instead of the original input space. By GDA therefore, both dimensionality reduction of the input feature space and selection of the useful discriminating features can be achieved simultaneously.
SVM, which was first proposed by Vapnik [25], has been considered as an effective classification scheme in many pattern recognition problems recently [22], [23], [24], [26], [27]. It is often reported that SVM provides better classification results than other widely used methods such as the neural network classifiers [28], [29]. This is partly because SVM aims to obtain the optimal answer using the available information and in the same time it shows better generalization ability on the unseen data.
In continue the details of the proposed algorithm for cardiac arrhythmia classification from the HRV signal is presented. Section 2 provides the overall block diagram of the proposed algorithm together with the details of each block. The results of the application of the proposed algorithm to the MIT-BIH arrhythmia database are presented in Section 3. Section 4, compares the results obtained by the proposed algorithm to those obtained by the other previously reported techniques. This is followed by a discussion on the results and the methods. Finally, Section 5 concludes the paper.
Section snippets
Database
The HRV data used in this work is generated from the ECG signals provided by the MIT-BIH Arrhythmia Database [30]. The database was created in 1980 as a reference standard for serving all those who are conducting a research on the cardiac arrhythmia detection and classification problem [31].
The MIT-BIH Arrhythmia Database includes 48 ECG recordings each of length 30 min with a total of about 109,000 R–R intervals. The ECG signals were bandpass-filtered in the frequency range of 0.1–100 Hz and
Results
To evaluate the performance of the proposed arrhythmia classification algorithm, a total number of 1367 HRV segments are used which includes 835 NSR segments, 57 PVC segments, 322 AF segments, 50 SSS segments, 78 VF segments, and 25 BII segments. The relatively high percentage of the NSR segments in the data set is not far from reality as ECG recordings usually have a higher percentage of normal beats compared to arrhythmic segments. The HRV signals at each class are randomly divided into the
Discussion
This section presents the comparative discussions over the performances of the feature reduction techniques, classification techniques, and the whole arrhythmia classification procedures.
Conclusions
In this paper, an effective HRV-based cardiac arrhythmia classification algorithm was presented. Initially, 15 original features were extracted from the input HRV signals including 8 linear features (7 time domain features and 1 frequency domain feature) and 7 nonlinear features. These features were used for discriminating six different types of cardiac arrhythmia by means of the SVM classifier. In order to reduce the learning time and also to improve the learning efficiency of the classifier,
References (51)
- et al.
Automatic arrhythmia detection based on time and time–frequency analysis of heart rate variability
Comp Meth Prog Biomed
(2004) - et al.
Atrial fibrillation classification with artificial neural networks
Pattern Recogn
(2007) - et al.
A Bayesian classification of heart rate variability data
Physica A
(2004) - et al.
An arrhythmia classification system based on the RR-interval signal
Artif. Intell. Med.
(2005) - et al.
A cascade learning system for classification of diabetes disease: generalized discriminant analysis and least square support vector machine
Expert Syst Appl
(2008) - et al.
A novel approach to estimation of E. coli promoter gene sequences: combining feature selection and least square support vector machine (FS_LSSVM)
Appl Math Comput
(2007) - et al.
Determining Lyapunov exponents from a time series
Physica D
(1985) - Task force of the European society of cardiology and the North American society of pacing and electrophysiology. Heart...
- et al.
Comparison of four techniques for recognition of ventricular fibrillation from the surface ECG
Med Biol Eng Comput
(1993) - et al.
Recognition of ventricular fibrillation using neural networks
Med Biol Eng Comput
(1994)
Artificial neural networks for the diagnosis of atrial fibrillation
Med Biol Eng Comput
Real-time discrimination of ventricular tachyarrhythmia with Fourier transform neural network
IEEE Trans Biomed Eng
A short-time multifractal approach for arrhythmia detection based on fuzzy neural network
IEEE Trans Biomed Eng
Classification of cardiac abnormalities using heart rate signals
Med Biol Eng Comput
Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features
IEEE Trans Biomed Eng
Detection of life-threatening cardiac arrhythmias using wavelet transformation
Med Biol Eng Comput
Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias
Med Biol Eng Comput
Support vector machine based arrhythmia classification using reduced features
Int J Control Automat Syst
A method for arrhythmic episode classification in ECGs using fuzzy logic and Markov models
Classification of heart rate data using artificial neural network and fuzzy equivalence relation
Pattern Recogn
Generalized discriminant analysis using a kernel approach
Neural Comput
Improving kernel Fisher discriminant analysis for face recognition
IEEE Trans Circuits Syst Video Technol
Kernel eigenfaces vs. kernel Fisherfaces: face recognition using kernel methods
Face recognition using kernel direct discriminant analysis algorithms
IEEE Trans Neural Networks
Recognition of electromyographic signals using cascaded kernel learning machine
IEEE/ASME Trans Mechatron
Cited by (316)
Optimization-enabled deep convolutional neural network with multiple features for cardiac arrhythmia classification using ECG signals
2024, Biomedical Signal Processing and ControlA lightweight hybrid CNN-LSTM explainable model for ECG-based arrhythmia detection
2024, Biomedical Signal Processing and ControlMalignant arrhythmias detection using a synthesis-by-analysis modeling method of arterial blood pressure signal
2024, Medical Engineering and PhysicsDetection of pulmonary arterial hypertension associated with congenital heart disease based on time–frequency domain and deep learning features
2023, Biomedical Signal Processing and ControlDetection of pulmonary hypertension associated with congenital heart disease based on time-frequency domain and deep learning features
2023, Biomedical Signal Processing and ControlDetection of ECG Wave Components for the Prediction of Acute Coronary Syndrome - Brief Survey
2024, International Journal of Intelligent Systems and Applications in Engineering