An efficient and automatic ECG arrhythmia diagnosis system using DWT and HOS features and entropy- based feature selection procedure

Abstract

Primary recognition of heart diseases by exploiting computer aided diagnosis (CAD) machines, decreases the vast rate of fatality among cardiac patients. Recognition of heart abnormalities is a staggering task because the low changes in ECG signals may not be exactly specified with eyesight. In this paper, an efficient approach for ECG arrhythmia diagnosis is proposed based on a combination of discrete wavelet transform and higher order statistics feature extraction and entropy based feature selection methods. Using the neural network and support vector machine, five classes of heartbeat categories are classified. Applying the neural network and support vector machine method, our proposed system is able to classify the arrhythmia classes with high accuracy (99.83%) and (99.03%), respectively. The advantage of the presented procedure has been experimentally demonstrated compared to the other recently presented methods in terms of accuracy.

Electrocardiogram signals classification using discrete wavelet transform and support vector machine classifier

“…In the literature many are the features and the methods used to characterize a signal especially for the ECG signals, there are the morphological features such as the PR, RR and QRS intervals, which are determined basically by the detection of the QRS complex [12]- [14]. There are also statistical features such as maximum, minimum, mean, variance and standard deviation [15], [16], [32], [33].…”

“…Gustavo et al [11] propose a comparison between several methods to remove baseline wander. Also the choice of features and the method used for its extraction affect directly the quality of signal characterization, some techniques are based on the extraction of morphological features such as the detection of the QRS complex proposed by Jiapu [12], the calculation of R-R intervals and peaks detection mentioned in the works of Shanti Chandra et al [13] and Priyanka [14], other methods are based on the extraction of the statistical ones or even a mixture of DWT and statistical features, Abdullah et al [15], or morphological and statistical features, Sahoo et al [16].…”

Analysis and classification of ECG beat based on wavelet decomposition and SVM

“…The ECG pre-processing stage, prior to ECG feature extraction involves in segmentation, baseline correction and alignment of ECG (4,13,14) . ECG segmentation is done to separate the ECG beat under consideration.…”

“…The normalized relative wavelet energy at each level is computed for all wavelet coefficients as given in the equation (4). The mean± standard deviation (std) for the coefficients is calculated and this serves as the meaningful feature of ECG beat.…”

“…To assist the medical doctors, researchers have proposed many algorithms for segmentation and classification of ECG signals more precisely and correctly in real-time (3) . An arrhythmia classification system includes the pre-processing of ECG signal, abnormal beat segmentation, extraction of wavelet domain features and beat classification (4)(5)(6) . The objective is to identify the various ECG arrhythmias as per AAMI standard thereby assisting the cardiologist for early diagnosis of heart disease.…”