Automatic detection of coronary artery disease (CAD) in an ECG signal

Ayesha Alhosani, Sara Alshizawi, Shayma Alali, Hani Saleh, Tasneem Assaf, Thanos Stouraitis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Coronary Artery Disease (CAD) is one of the most common deadly heart diseases. In this paper, several CAD diagnosis methodologies and their limitations are discussed. Moreover, a full system for automated CAD diagnoses is built and evaluated. A linear feature extraction method for Electrocardiography (ECG) signals classification is proposed. Simulation results show that the proposed method achieves an accuracy above 90%.

Original languageBritish English
Title of host publicationICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages414-418
Number of pages5
ISBN (Electronic)9781538619117
DOIs
StatePublished - 14 Feb 2018
Event24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017 - Batumi, Georgia
Duration: 5 Dec 20178 Dec 2017

Publication series

NameICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems
Volume2018-January

Conference

Conference24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017
Country/TerritoryGeorgia
CityBatumi
Period5/12/178/12/17

Keywords

  • Coronary Artery Disease (CAD)
  • Detection
  • ECG
  • Linear Feature Extraction
  • Verilog

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