Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal

•Classification of normal, CAD, MI and CHF classes using ECG beat is proposed•CWT transform is performed on ECG beat•Contourlet and shearlet transforms are performed on scalogram•First and second order statistical features are extracted•Obtained an accuracy of 99.55% using contourlet transform Undia...

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Published in:	Knowledge-based systems Vol. 132; pp. 156 - 166
Main Authors:	Acharya, U Rajendra, Fujita, Hamido, Sudarshan, Vidya K, Oh, Shu Lih, Adam, Muhammad, Tan, Jen Hong, Koo, Jie Hui, Jain, Arihant, Lim, Choo Min, Chua, Kuang Chua
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 15.09.2017 Elsevier Science Ltd
Subjects:	Abnormalities Algorithms Automatic classification Averages Blood flow Cardiovascular disease Classifiers Congestive heart failure Continuous wavelet transform Contourlet and shearlet transforms Coronary artery disease Decision making Deviation Diagnostic systems Diagnostic tests Disease Echocardiography Electrocardiogram Electrocardiography Entropy Entropy (Information theory) Feature extraction Function words Heart failure Hospitals Kurtosis Muscles Myocardial infarction Optimization Particle swarm optimization Ultrasonic imaging Undiagnosed Variance analysis Myocardial infarction Contourlet and shearlet transforms Congestive heart failure Coronary artery disease Electrocardiogram Continuous wavelet transform
ISSN:	0950-7051, 1872-7409
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Abstract	•Classification of normal, CAD, MI and CHF classes using ECG beat is proposed•CWT transform is performed on ECG beat•Contourlet and shearlet transforms are performed on scalogram•First and second order statistical features are extracted•Obtained an accuracy of 99.55% using contourlet transform Undiagnosed coronary artery disease (CAD) progresses rapidly and leads to myocardial infarction (MI) by reducing the blood flow to the cardiac muscles. Timely diagnosis of MI and its location is significant, else, it expands and may impair the left ventricular (LV) function. Thus, if CAD and MI are not picked up by electrocardiogram (ECG) during diagnostic test, it can lead to congestive heart failure (CHF). Therefore, in this paper, the characterization of three cardiac abnormalities namely, CAD, MI and CHF are compared. Performance of novel algorithms is based on contourlet and shearlet transformations of the ECG signals. Continuous wavelet transform (CWT) is performed on normal, CAD, MI and CHF ECG beat to obtain scalograms. Subsequently, contourlet and shearlet transformations are applied on the scalograms to obtain the respective coefficients. Entropies, first and second order statistical features namely, mean (Mni), min (Mini), max (Mxi), standard deviation (Dsti), average power (Pavgi), inter-quartile range (IQRi), Shannon entropy (Eshi), mean Tsallis entropy (Emtsi), kurtosis (Kuri), mean absolute deviation (MADi), and mean energy (Ωmi), are extracted from each contourlet and shearlet coefficients. Only significant features are selected using improved binary particle swarm optimization (IBPSO) feature selection method. Selected features are ranked using analysis of variance (ANOVA) and relieff techniques. The highly ranked features are subjected to decision tree (DT) and K-nearest neighbor (KNN) classifiers. Proposed method has achieved accuracy, sensitivity and specificity of (i) 99.55%, 99.93% and 99.24% using contourlet transform, and (ii) 99.01%, 99.82% and 98.75% using shearlet transform. Among the two proposed techniques, contourlet transform method performed marginally better than shearlet transform technique in classifying the four classes. The proposed CWT combined with contourlet-based technique can be implemented in hospitals to speed up the diagnosis of three different cardiac abnormalities using a single ECG test. This technique, minimizes the unnecessary diagnostic tests required to confirm the diagnosis.
AbstractList	Undiagnosed coronary artery disease (CAD) progresses rapidly and leads to myocardial infarction (MI) by reducing the blood flow to the cardiac muscles. Timely diagnosis of MI and its location is significant, else, it expands and may impair the left ventricular (LV) function. Thus, if CAD and MI are not picked up by electrocardiogram (ECG) during diagnostic test, it can lead to congestive heart failure (CHF). Therefore, in this paper, the characterization of three cardiac abnormalities namely, CAD, MI and CHF are compared. Performance of novel algorithms is based on contourlet and shearlet transformations of the ECG signals. Continuous wavelet transform (CWT) is performed on normal, CAD, MI and CHF ECG beat to obtain scalograms. Subsequently, contourlet and shearlet transformations are applied on the scalograms to obtain the respective coefficients. Entropies, first and second order statistical features namely, mean (Min), min (Min), max (Mix), standard deviation (Dist), average power (Piavg), inter-quartile range (IQRi ), Shannon entropy (Eish), mean Tsallis entropy (Eimt s), kurtosis (Kiur), mean absolute deviation (MiAD), and mean energy (Ωim), are extracted from each contourlet and shearlet coefficients. Only significant features are selected using improved binary particle swarm optimization (IBPSO) feature selection method. Selected features are ranked using analysis of variance (ANOVA) and relief techniques. The highly ranked features are subjected to decision tree (DT) and K-nearest neighbor (KNN) classifiers. Proposed method has achieved accuracy, sensitivity and specificity of (i) 99.55%, 99.93% and 99.24% using contourlet transform, and (ii) 99.01%, 99.82% and 98.75% using shearlet transform. Among the two proposed techniques, contourlet transform method performed marginally better than shearlet transform technique in classifying the four classes. The proposed CWT combined with contourlet-based technique can be implemented in hospitals to speed up the diagnosis of three different cardiac abnormalities using a single ECG test. This technique, minimizes the unnecessary diagnostic tests required to confirm the diagnosis. •Classification of normal, CAD, MI and CHF classes using ECG beat is proposed•CWT transform is performed on ECG beat•Contourlet and shearlet transforms are performed on scalogram•First and second order statistical features are extracted•Obtained an accuracy of 99.55% using contourlet transform Undiagnosed coronary artery disease (CAD) progresses rapidly and leads to myocardial infarction (MI) by reducing the blood flow to the cardiac muscles. Timely diagnosis of MI and its location is significant, else, it expands and may impair the left ventricular (LV) function. Thus, if CAD and MI are not picked up by electrocardiogram (ECG) during diagnostic test, it can lead to congestive heart failure (CHF). Therefore, in this paper, the characterization of three cardiac abnormalities namely, CAD, MI and CHF are compared. Performance of novel algorithms is based on contourlet and shearlet transformations of the ECG signals. Continuous wavelet transform (CWT) is performed on normal, CAD, MI and CHF ECG beat to obtain scalograms. Subsequently, contourlet and shearlet transformations are applied on the scalograms to obtain the respective coefficients. Entropies, first and second order statistical features namely, mean (Mni), min (Mini), max (Mxi), standard deviation (Dsti), average power (Pavgi), inter-quartile range (IQRi), Shannon entropy (Eshi), mean Tsallis entropy (Emtsi), kurtosis (Kuri), mean absolute deviation (MADi), and mean energy (Ωmi), are extracted from each contourlet and shearlet coefficients. Only significant features are selected using improved binary particle swarm optimization (IBPSO) feature selection method. Selected features are ranked using analysis of variance (ANOVA) and relieff techniques. The highly ranked features are subjected to decision tree (DT) and K-nearest neighbor (KNN) classifiers. Proposed method has achieved accuracy, sensitivity and specificity of (i) 99.55%, 99.93% and 99.24% using contourlet transform, and (ii) 99.01%, 99.82% and 98.75% using shearlet transform. Among the two proposed techniques, contourlet transform method performed marginally better than shearlet transform technique in classifying the four classes. The proposed CWT combined with contourlet-based technique can be implemented in hospitals to speed up the diagnosis of three different cardiac abnormalities using a single ECG test. This technique, minimizes the unnecessary diagnostic tests required to confirm the diagnosis.
Author	Oh, Shu Lih Tan, Jen Hong Chua, Kuang Chua Fujita, Hamido Adam, Muhammad Lim, Choo Min Acharya, U Rajendra Koo, Jie Hui Jain, Arihant Sudarshan, Vidya K
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Keywords	Myocardial infarction Contourlet and shearlet transforms Congestive heart failure Coronary artery disease Electrocardiogram Continuous wavelet transform
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Snippet	•Classification of normal, CAD, MI and CHF classes using ECG beat is proposed•CWT transform is performed on ECG beat•Contourlet and shearlet transforms are... Undiagnosed coronary artery disease (CAD) progresses rapidly and leads to myocardial infarction (MI) by reducing the blood flow to the cardiac muscles. Timely...
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SubjectTerms	Abnormalities Algorithms Automatic classification Averages Blood flow Cardiovascular disease Classifiers Congestive heart failure Continuous wavelet transform Contourlet and shearlet transforms Coronary artery disease Decision making Deviation Diagnostic systems Diagnostic tests Disease Echocardiography Electrocardiogram Electrocardiography Entropy Entropy (Information theory) Feature extraction Function words Heart failure Hospitals Kurtosis Muscles Myocardial infarction Optimization Particle swarm optimization Ultrasonic imaging Undiagnosed Variance analysis
Title	Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal
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