Automated diagnosis of Coronary Artery Disease using pattern recognition approach

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

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

4 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 languageEnglish
Title of host publication2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationSmarter Technology for a Healthier World, EMBC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages434-437
Number of pages4
ISBN (Electronic)9781509028092
DOIs
Publication statusPublished - 13-09-2017
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of
Duration: 11-07-201715-07-2017

Conference

Conference39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
CountryKorea, Republic of
CityJeju Island
Period11-07-1715-07-17

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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