Entropies for automated detection of coronary artery disease using ECG signals: A review

• Autorzy: Acharya U. R. , Hagiwara Y. , Koh J. E. W. , Oh S. L. , Tan J. H. , Adam M. , Tan R. S.

Identyfikatory

DOI

10.1016/j.bbe.2018.03.001

• Języki publikacji: EN

Abstrakty

EN

Coronary artery disease (CAD) develops when coronary arteries are unable to supply oxygen-rich blood to the heart due to the accumulation of cholesterol plaque on the inner walls of the arteries. Chronic insufficient blood flow leads to the complications, including angina and heart failure. In addition, acute plaque rupture may lead to vessel occlusion, causing a heart attack. Thus, it is encouraged to have regular check-ups to diagnose CAD early and avert complications. The electrocardiogram (ECG) is a widely used diagnostic tool to study the electrical activity of the heart. However, ECG signals are highly chaotic, complex, and non-stationary in their behaviour. It is laborious, and requires expertise, to visually interpret these signals. Hence, the computer-aided detection system (CADS) is developed to assist clinicians to interpret the ECG signals fast and reliably. In this work, we have employed sixteen entropies to extract the various hidden signatures from ECG signals of normal healthy persons as well as patients with CAD. We observed that the majority of extracted entropy features showed lower values for CAD patients compared to normal subjects. We believe that there is one possible reason which could be the decreased in the variability of ECG signals is associated with reduced heart pump function.

Słowa kluczowe

EN

electrocardiogram signal; entropy features; coronary artery disease

PL

elektrokardiogram; wybór cech; choroba wieńcowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 2
• Strony: 373--384
• Opis fizyczny: Bibliogr. 82 poz., rys., tab., wykr.

Twórcy

autor

Acharya U. R.

• Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia

autor

Hagiwara Y.

• Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore

autor

Koh J. E. W.

• Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore

autor

Oh S. L.

• Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore

autor

Tan J. H.

• Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore

autor

Adam M.

• Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore

autor

Tan R. S.

• National Heart Centre Singapore, Singapore; Duke-National University of Singapore Medical School, Singapore

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).