TY - GEN
T1 - Automated diagnosis of Coronary Artery Disease using pattern recognition approach
AU - Desai, Usha
AU - Nayak, C. Gurudas
AU - Seshikala, G.
AU - Martis, Roshan J.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/13
Y1 - 2017/9/13
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.
UR - https://www.scopus.com/pages/publications/85032194107
UR - https://www.scopus.com/pages/publications/85032194107#tab=citedBy
U2 - 10.1109/EMBC.2017.8036855
DO - 10.1109/EMBC.2017.8036855
M3 - Conference contribution
AN - SCOPUS:85032194107
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 434
EP - 437
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Y2 - 11 July 2017 through 15 July 2017
ER -
