Multi-Lead ECG Signal Analysis for Myocardial Infarction Detection and Localization Through the Mapping of Grassmannian and Euclidean Features Into a Common Hilbert Space

Abstract:


Electrocardiogram is commonly used as a diagnostic tool for the monitoring of cardiac health and the detection of possible heart diseases. However, the procedure followed for the diagnosis of heart abnormalities is time consuming and prone to human errors. Thus, the development of computer-aided techniques for the automatic analysis of electrocardiogram signals is of vital importance for the diagnosis and prevention of heart diseases. The most serious outcome of coronary heart disease is the myocardial infarction, i.e., the rapid and irreversible damage of cardiac muscles, which, if not diagnosed and treated in time, continues to damage further the myocardial structure and function. In this paper we propose a novel approach for the automatic detection and localization of myocardial infarction from multi-lead electrocardiogram signals.


  • P. Barmpoutis, K. Dimitropoulos, A. Apostolidis, N. Grammalidis, "Multi-Lead ECG Signal Analysis for Myocardial Infarction Detection and Localization Through the Mapping of Grassmannian and Euclidean Features Into a Common Hilbert Space", Biomedical Signal Processing and Control, Volume 52, pp. 111-119, 2019.

  • Full document available here.
    Contact Information

    Dr. Petros Daras, Research Director
    6th km Charilaou – Thermi Rd, 57001, Thessaloniki, Greece
    P.O.Box: 60361
    Tel.: +30 2310 464160 (ext. 156)
    Fax: +30 2310 464164
    Email: daras(at)iti(dot)gr