<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Journal of Electrical Bioimpedance

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Primary recognition of heart diseases by exploiting computer aided diagnosis (CAD) machines, decreases the vast rate of fatality among cardiac patients....

Classifier	Sensitivity (%)	Specificity (%)	Accuracy (%)
SVM-RBF	99.57	99.89	99.83
NN	97.58	99.39	99.03

Literature	year	Features	Classifier	Classes	Accuracy (%)
Martis et al. [20]	2012	PCA	SVM-RBF	5	98.11%
Martis et al. [14]	2013	DWT + PCA	SVM-RBF	5	96.92%
		DWT + PCA	NN	5	98.78%
Osowski and Linh [19]	2001	HOS	Hybrid fuzzy	7	96.06%
			NN
Martis et al. [27]	2013	Cumulant + PCA	NN	5	94.52%
Elhaj et al. [24]	2016	PCA + DWT + HOS +	SVM-RBF	5	98.91%
		ICA
			NN	5	98.90%
Acharya et al [28]	2017	Raw data	CNN	5	94.03
Yang et al. [29]	2018	PCAnet	Linear SVM	5	97.94
Oh et al. [30]	2018	Raw data	CNN-LSTM	5	98.10
Proposed		DWT+HOS	SVM-RBF	5	99.83
			NN	5	99.03

ANSI/AAMI classes	Non-ectopic (N)	Supraventricular (S)	Ventricular (V)	Fusion (F)	Unknown (U)

	Normal (N)	Aberrated atrial premature (A)	Ventricular escape (V)	Fusion of ventricular and normal (F)	Unclassifiable (U)

	Left bundle branch block (LBBB)	Atrial premature (a)	Premature ventricular contraction (E)		Paced (p)

MIT-BIH classes	Right bundle branch block (RBBB)	Supraventricular premature (S)			Fusion of paced and normal (f)

	Nodal (junctional) escape (j)	Nodal (junctional) premature (J)

	Atrial escape beat (e)

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

Published Online: Aug 20, 2019

Page range: 47 - 54

Received: Feb 19, 2019

DOI: https://doi.org/10.2478/joeb-2019-0007

Keywords
ECG, classification, feature extraction, feature selection, entropy

© 2019 Chashmi, Amirani, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1

Fig. 2

Classification results of the proposed methods with the two different classifiers.

– Comparison of the classification efficiency of the proposed method and some of studies performed based on the same database.

A summary table of ECG heartbeats classified as per ANSI/AAMI EC57:1998 standard [25].

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

Published Online: Aug 20, 2019

Page range: 47 - 54

Received: Feb 19, 2019

DOI: https://doi.org/10.2478/joeb-2019-0007

KeywordsECG, classification, feature extraction, feature selection, entropy

© 2019 Chashmi, Amirani, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1

Fig. 2

Classification results of the proposed methods with the two different classifiers.

– Comparison of the classification efficiency of the proposed method and some of studies performed based on the same database.

A summary table of ECG heartbeats classified as per ANSI/AAMI EC57:1998 standard [25].

Keywords
ECG, classification, feature extraction, feature selection, entropy