Arrhythmia ECG Beats Classification Using Wavelet-Based Features and Support Vector Machine Classifier

Arrhythmia ECG Beats Classification Using Wavelet-Based Features and Support Vector Machine Classifier

Chandan Kumar Jha (Indian Institute of Technology Patna, India) and Maheshkumar H. Kolekar (Indian Institute of Technology Patna, India)
Copyright: © 2019 |Pages: 15
DOI: 10.4018/978-1-5225-7796-6.ch004
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Abstract

Abnormal behavior of heart muscles generates irregular heartbeats which are collectively known as arrhythmia. Classification of arrhythmia beats plays a prominent role in electrocardiogram (ECG) analysis. It is widely used in online and long-term patient monitoring systems. This chapter reports a classification technique to recognize normal (N) and five arrhythmia beats (i.e., left bundle branch block [LBBB], right bundle branch block [RBBB], premature ventricular contraction [V], paced [P], and atrial premature contraction [A]). The technique utilizes features of heartbeats extracted by the wavelet multi-resolution analysis. The feature vectors are used to train and test the classifier based on the support vector machine which has been emerged as a benchmark in machine learning classifier. It accomplishes the beat classification very efficiently. ECG records of the MIT-BIH arrhythmia database are utilized to acquire the different types of heartbeats. Performance of the proposed classifier outperforms the contemporary arrhythmia beats classification techniques.
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Introduction

In recent years, the incidence and prevalence of cardiovascular diseases (CVD) have gained an expanding growth. As per the recent report of WHO, the overall death rates due to CVD have decreased but the burden of heart diseases still remains at peak (Martis et al., 2013). Among many complications of CVD, atrial and ventricular arrhythmias are one of them which occur due to the abnormal electrical activity of heart muscles. Abnormal behavior of heart muscles generates different abnormal cardiac rhythms which are collectively known as arrhythmia. Ventricular fibrillation and flutter are some arrhythmia types which are life-threatening. It results in medical emergencies lead to cardiac arrest, hemodynamic collapse and sudden cardiac death (Huikuri et al., 2001).

The electrical activity of heart muscles could be monitored non-invasively using Electrocardiogram (ECG). Due to the small amplitude and duration, it is difficult to interpret the concealed information present in the ECG data. Therefore, a computer-assisted diagnostic tool could help the physicians in their practice and interpretation. The computer-assisted diagnosis tools can play a major role in the cardiovascular disease management (Hadhoud et al., 2006). These tools are based on many classifiers such neural network (Gutiérrez-Gnecchi et al., 2017), support vector machine (Übeyli, et al., 2007; Kolekar et al., 2015), deterministic learning (Dong et al., 2017), hidden Markov model (Liang et al., 2014; Dash et al., 2017), Bayesian network (de Oliveira et al. 2011; Kolekar et al., 2015; Kolekar et al., 2011), linear discriminant analysis (Yeh et al., 2009) etc. These classifiers are widely used for arrhythmia beat classification. A classifier based on the generalized linear model and the auto-regressive model was developed in (Ge et al., 2002) which claims 93.2% classification accuracy for six types of arrhythmia beats. A neuro-fuzzy technique with Hermite coefficients was used in (Linh et al., 2003) to classify ECG beats with 96% accuracy. In (Martis et. al, 2009), a Gaussian mixture model-based classification scheme was proposed for normal and abnormal ECG with 94% accuracy. An automated screening tool based on support vector machine classifier was proposed in (Martis et al., 2012) to distinguish normal and abnormal ECG beats with 95.6% accuracy.

Table 1.
Different arrhythmia beats classifier and features
LiteratureClassifierFeaturesClasses
Acir et. al., 2005Least-square SVMTemporal + spectral6
Melgani et al., 2008SVMTemporal + morphological6
Chua et. al., 2009 SVMHigher order spectral5
Kutlu et. al., 2012Neural networkHigher order statistics5
Martis et. al., 2013 SVMSpectral+PCA5
Martis et. al., 2013 Least-square SVMSpectral+PCA5
Mert et. al, 2014 Decision treeTemporal6
Khalaf et. al., 2015 SVMSpectral correlation5
Thomas et. al., 2015 Artificial neural networkDWT + morphological5
Shadmand et. al., 2016Optimized block based neural networkHermite coefficients + temporal5
Li et. al., 2017 Regression neural networkMorphological5

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