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Biomedical Signal Processing and Control
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Biomedical Signal Processing and Control
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Multi-lead ECG signal analysis for myocardial infarction detection and localization through the mapping of Grassmannian and Euclidean features into a common Hilbert space

Authors: Panagiotis Barmpoutis; Kosmas Dimitropoulos; Anestis Apostolidis; Nikos 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

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.

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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