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

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

doi:10.1109/ICECS.2017.8292036

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

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

Electrical Engineering

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

4 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 language	British English
Title of host publication	ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	414-418
Number of pages	5
ISBN (Electronic)	9781538619117
DOIs	https://doi.org/10.1109/ICECS.2017.8292036
State	Published - 14 Feb 2018
Event	24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017 - Batumi, Georgia Duration: 5 Dec 2017 → 8 Dec 2017

Publication series

Name	ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems
Volume	2018-January

Conference

Conference	24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017
Country/Territory	Georgia
City	Batumi
Period	5/12/17 → 8/12/17

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

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

Access to Document

10.1109/ICECS.2017.8292036

OpenUrl availability

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Cite this

Alhosani, A., Alshizawi, S., Alali, S., Saleh, H., Assaf, T., & Stouraitis, T. (2018). Automatic detection of coronary artery disease (CAD) in an ECG signal. In ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems (pp. 414-418). (ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECS.2017.8292036

Alhosani, Ayesha ; Alshizawi, Sara ; Alali, Shayma et al. / Automatic detection of coronary artery disease (CAD) in an ECG signal. ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 414-418 (ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems).

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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\%.",

keywords = "Coronary Artery Disease (CAD), Detection, ECG, Linear Feature Extraction, Verilog",

author = "Ayesha Alhosani and Sara Alshizawi and Shayma Alali and Hani Saleh and Tasneem Assaf and Thanos Stouraitis",

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Alhosani, A, Alshizawi, S, Alali, S, Saleh, H, Assaf, T & Stouraitis, T 2018, Automatic detection of coronary artery disease (CAD) in an ECG signal. in ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems. ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 414-418, 24th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2017, Batumi, Georgia, 5/12/17. https://doi.org/10.1109/ICECS.2017.8292036

Automatic detection of coronary artery disease (CAD) in an ECG signal. / Alhosani, Ayesha; Alshizawi, Sara; Alali, Shayma et al.
ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems. Institute of Electrical and Electronics Engineers Inc., 2018. p. 414-418 (ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems; Vol. 2018-January).

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

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AU - Assaf, Tasneem

AU - Stouraitis, Thanos

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N2 - 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%.

AB - 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%.

KW - Coronary Artery Disease (CAD)

KW - Detection

KW - ECG

KW - Linear Feature Extraction

KW - Verilog

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M3 - Conference contribution

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BT - ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems

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Y2 - 5 December 2017 through 8 December 2017

ER -

Alhosani A, Alshizawi S, Alali S, Saleh H, Assaf T, Stouraitis T. Automatic detection of coronary artery disease (CAD) in an ECG signal. In ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems. Institute of Electrical and Electronics Engineers Inc. 2018. p. 414-418. (ICECS 2017 - 24th IEEE International Conference on Electronics, Circuits and Systems). doi: 10.1109/ICECS.2017.8292036

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

OpenUrl availability

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