Automated diagnosis of Coronary Artery Disease using pattern recognition approach

Usha Desai; C. Gurudas Nayak; G. Seshikala; Roshan J. Martis

doi:10.1109/EMBC.2017.8036855

Automated diagnosis of Coronary Artery Disease using pattern recognition approach

Usha Desai, C. Gurudas Nayak, G. Seshikala, Roshan J. Martis

Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

Coronary Artery Disease (CAD) is the most leading Cardiovascular Disease (CVD), which results due to buildup of plaque inside the coronary arteries. The CAD and Normal Sinus Rhythm (NSR) heartbeats can be discriminated and diagnosed noninvasively using the standard tool Electrocardiogram (ECG). However, manual diagnosis of ECG is tiresome and time consuming task, due to complex nature and unseen nonlinearities of ECG. Hence an automated system plays a substantial role. In this study, CAD and NSR heartbeats are discriminated and diagnosed using Higher-Order Statistics (HOS) cumulants features. Further, the cumulants coefficients dimensionality reduced using Principal Components Analysis (PCA) and the medically significant features (p-value<0.05) Principal Components (PCs) are subjected for classification using Random Forest (RAF) and Rotation Forest (ROF) ensemble classifiers. Proposed system is robust which helps in screening CAD risk factors and telemonitoring applications.

Original language	English
Title of host publication	2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Smarter Technology for a Healthier World, EMBC 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	434-437
Number of pages	4
ISBN (Electronic)	9781509028092
DOIs	https://doi.org/10.1109/EMBC.2017.8036855
Publication status	Published - 13-09-2017
Event	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of Duration: 11-07-2017 → 15-07-2017

Conference

Conference	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	11-07-17 → 15-07-17

All Science Journal Classification (ASJC) codes

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/EMBC.2017.8036855

Cite this

Desai, U., Nayak, C. G., Seshikala, G., & Martis, R. J. (2017). Automated diagnosis of Coronary Artery Disease using pattern recognition approach. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings (pp. 434-437). [8036855] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2017.8036855

Desai, Usha ; Nayak, C. Gurudas ; Seshikala, G. et al. / Automated diagnosis of Coronary Artery Disease using pattern recognition approach. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 434-437

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abstract = "Coronary Artery Disease (CAD) is the most leading Cardiovascular Disease (CVD), which results due to buildup of plaque inside the coronary arteries. The CAD and Normal Sinus Rhythm (NSR) heartbeats can be discriminated and diagnosed noninvasively using the standard tool Electrocardiogram (ECG). However, manual diagnosis of ECG is tiresome and time consuming task, due to complex nature and unseen nonlinearities of ECG. Hence an automated system plays a substantial role. In this study, CAD and NSR heartbeats are discriminated and diagnosed using Higher-Order Statistics (HOS) cumulants features. Further, the cumulants coefficients dimensionality reduced using Principal Components Analysis (PCA) and the medically significant features (p-value<0.05) Principal Components (PCs) are subjected for classification using Random Forest (RAF) and Rotation Forest (ROF) ensemble classifiers. Proposed system is robust which helps in screening CAD risk factors and telemonitoring applications.",

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Desai, U, Nayak, CG, Seshikala, G & Martis, RJ 2017, Automated diagnosis of Coronary Artery Disease using pattern recognition approach. in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings., 8036855, Institute of Electrical and Electronics Engineers Inc., pp. 434-437, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, Jeju Island, Korea, Republic of, 11-07-17. https://doi.org/10.1109/EMBC.2017.8036855

Automated diagnosis of Coronary Artery Disease using pattern recognition approach. / Desai, Usha; Nayak, C. Gurudas; Seshikala, G. et al.
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 434-437 8036855.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Desai, Usha

AU - Nayak, C. Gurudas

AU - Seshikala, G.

AU - Martis, Roshan J.

PY - 2017/9/13

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N2 - Coronary Artery Disease (CAD) is the most leading Cardiovascular Disease (CVD), which results due to buildup of plaque inside the coronary arteries. The CAD and Normal Sinus Rhythm (NSR) heartbeats can be discriminated and diagnosed noninvasively using the standard tool Electrocardiogram (ECG). However, manual diagnosis of ECG is tiresome and time consuming task, due to complex nature and unseen nonlinearities of ECG. Hence an automated system plays a substantial role. In this study, CAD and NSR heartbeats are discriminated and diagnosed using Higher-Order Statistics (HOS) cumulants features. Further, the cumulants coefficients dimensionality reduced using Principal Components Analysis (PCA) and the medically significant features (p-value<0.05) Principal Components (PCs) are subjected for classification using Random Forest (RAF) and Rotation Forest (ROF) ensemble classifiers. Proposed system is robust which helps in screening CAD risk factors and telemonitoring applications.

AB - Coronary Artery Disease (CAD) is the most leading Cardiovascular Disease (CVD), which results due to buildup of plaque inside the coronary arteries. The CAD and Normal Sinus Rhythm (NSR) heartbeats can be discriminated and diagnosed noninvasively using the standard tool Electrocardiogram (ECG). However, manual diagnosis of ECG is tiresome and time consuming task, due to complex nature and unseen nonlinearities of ECG. Hence an automated system plays a substantial role. In this study, CAD and NSR heartbeats are discriminated and diagnosed using Higher-Order Statistics (HOS) cumulants features. Further, the cumulants coefficients dimensionality reduced using Principal Components Analysis (PCA) and the medically significant features (p-value<0.05) Principal Components (PCs) are subjected for classification using Random Forest (RAF) and Rotation Forest (ROF) ensemble classifiers. Proposed system is robust which helps in screening CAD risk factors and telemonitoring applications.

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Desai U, Nayak CG, Seshikala G, Martis RJ. Automated diagnosis of Coronary Artery Disease using pattern recognition approach. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 434-437. 8036855 doi: 10.1109/EMBC.2017.8036855

Automated diagnosis of Coronary Artery Disease using pattern recognition approach

Abstract

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

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