Automated diagnosis of Coronary Artery Disease using nonlinear features extracted from ECG signals

Chaitra Sridhar, U. Rajendra Acharya, Hamido Fujita, G. Muralidhar Bairy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Coronary Artery Disease (CAD) is one of the hazardous heart disease which results in angina, Myocardial Infarction (MI) and Sudden Cardiac Death (SCD). CAD is a cardiac disorder in which a plague develops in the interior wall of the arteries resulting in blockage of blood reaching to the heart muscles. Electrocardiogram (ECG) is the cardiac signal which represents cardiac depolarisation and repolarisation regulated at the surface of the chest. The minute variations in amplitude and duration in the ECG wave specifies different pathological conditions which are tedious to interpret visually. Hence computer aided diagnostic systems are used to monitor ECG signals. In the present work, automated diagnosis of CAD is done using Discrete Wavelet Transform (DWT) and nonlinear feature extraction techniques like; Multivariate Multi-scale Entropy (MMSE), Tsallis entropy and renyi entropies. The extracted features after DWT are ranked based on t-value and fed to K Nearest Neighbour (KNN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Decision Tree (DT) classifiers for automated classification of normal and CAD classes. This technique provided the highest accuracy of 98.67% using KNN classifier. Hence, the proposed system can aid clinicians in faster and accurate diagnosis of CAD and thereby provide sufficient time for proper treatment.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages545-549
Number of pages5
ISBN (Electronic)9781509018970
DOIs
Publication statusPublished - 06-02-2017
Event2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Budapest, Hungary
Duration: 09-10-201612-10-2016

Conference

Conference2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
CountryHungary
CityBudapest
Period09-10-1612-10-16

Fingerprint

Coronary Artery Disease
Electrocardiography
Cardiac
Entropy
Discrete wavelet transforms
Wavelet Transform
Nearest Neighbor
Classifier
Repolarization
Classifiers
Tsallis Entropy
Probabilistic Neural Network
Rényi Entropy
Myocardial Infarction
Depolarization
Arteries
Decision tree
Muscle
Feature Extraction
Blood

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Control and Optimization
  • Human-Computer Interaction

Cite this

Sridhar, C., Acharya, U. R., Fujita, H., & Bairy, G. M. (2017). Automated diagnosis of Coronary Artery Disease using nonlinear features extracted from ECG signals. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings (pp. 545-549). [7844296] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2016.7844296
Sridhar, Chaitra ; Acharya, U. Rajendra ; Fujita, Hamido ; Bairy, G. Muralidhar. / Automated diagnosis of Coronary Artery Disease using nonlinear features extracted from ECG signals. 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 545-549
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Sridhar, C, Acharya, UR, Fujita, H & Bairy, GM 2017, Automated diagnosis of Coronary Artery Disease using nonlinear features extracted from ECG signals. in 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings., 7844296, Institute of Electrical and Electronics Engineers Inc., pp. 545-549, 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, Budapest, Hungary, 09-10-16. https://doi.org/10.1109/SMC.2016.7844296

Automated diagnosis of Coronary Artery Disease using nonlinear features extracted from ECG signals. / Sridhar, Chaitra; Acharya, U. Rajendra; Fujita, Hamido; Bairy, G. Muralidhar.

2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 545-549 7844296.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Coronary Artery Disease (CAD) is one of the hazardous heart disease which results in angina, Myocardial Infarction (MI) and Sudden Cardiac Death (SCD). CAD is a cardiac disorder in which a plague develops in the interior wall of the arteries resulting in blockage of blood reaching to the heart muscles. Electrocardiogram (ECG) is the cardiac signal which represents cardiac depolarisation and repolarisation regulated at the surface of the chest. The minute variations in amplitude and duration in the ECG wave specifies different pathological conditions which are tedious to interpret visually. Hence computer aided diagnostic systems are used to monitor ECG signals. In the present work, automated diagnosis of CAD is done using Discrete Wavelet Transform (DWT) and nonlinear feature extraction techniques like; Multivariate Multi-scale Entropy (MMSE), Tsallis entropy and renyi entropies. The extracted features after DWT are ranked based on t-value and fed to K Nearest Neighbour (KNN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Decision Tree (DT) classifiers for automated classification of normal and CAD classes. This technique provided the highest accuracy of 98.67% using KNN classifier. Hence, the proposed system can aid clinicians in faster and accurate diagnosis of CAD and thereby provide sufficient time for proper treatment.

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Sridhar C, Acharya UR, Fujita H, Bairy GM. Automated diagnosis of Coronary Artery Disease using nonlinear features extracted from ECG signals. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 545-549. 7844296 https://doi.org/10.1109/SMC.2016.7844296