Automatic Detection Of Electrocardiogram ST Segment: Application In Ischemic Disease Diagnosis

Abstract

The analysis of electrocardiograph (ECG) signal provides important clinical information for heart disease diagnosis. The ECG signal consists of the P, QRS complex, and T-wave. These waves correspond to the fields induced by specific electric phenomenon on the cardiac surface. Among them, the detection of ischemia can be achieved by analysis the ST segment. Ischemia is one of the most serious and prevalent heart diseases. In this paper, the European database was used for evaluation of automatic detection of the ST segment. The method comprises several steps; ECG signal loading from database, signal preprocessing, detection of QRS complex and R-peak, ST segment, and other relation parameter measurement. The developed application displays the results of the analysis.

Authors and Affiliations

Duck Lee, Jun Park, Jeasoon Choi, Ahmed Rabbi

Keywords

Related Articles

Bi-Objective Task Scheduling in Cloud Computing using Chaotic Bat Algorithm

Cloud computing is a technology for providing services over the Internet. It gives approach to renting IT infrastructures on a short-term pay- per-usage basis. One of the service provider’s goals is to use the resources...

El Niño / La Niña Identification based on Takens Reconstruction Theory

An identification method for earth observation data according to a chaotic behavior based on Takens reconstruction theory is proposed. The proposed method is examined by using the observed time series data of SST (Sea Su...

Risk Propagation Analysis and Visualization using Percolation Theory

This article presents a percolation-based approach for the analysis of risk propagation, using malware spreading as a showcase example. Conventional risk management is often driven by human (subjective) assessment of how...

Security and Privacy Risks Awareness for Bring Your Own Device (BYOD) Paradigm

The growing trend of BYOD in the higher education institutions creates a new form of student learning pedagogy in which students are able to use the mobile devices for their academic purposes in anywhere and anytime. Sec...

Feature Selection Based on Minimum Overlap Probability (MOP) in Identifying Beef and Pork

Feature selection is one of the most important techniques in image processing for classifying. In classifying beef and pork based on texture feature, feature overlaps are difficult issues. This paper proposed feature sel...

Download PDF file
  • EP ID EP87386
  • DOI 10.14569/IJACSA.2013.040222
  • Views 71
  • Downloads 0

How To Cite

Duck Lee, Jun Park, Jeasoon Choi, Ahmed Rabbi (2013). Automatic Detection Of Electrocardiogram ST Segment: Application In Ischemic Disease Diagnosis. International Journal of Advanced Computer Science & Applications, 4(2), 150-155. https://europub.co.uk/articles/-A-87386