publication . Article . Other literature type . 2020

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

Anestis Apostolidis; Panagiotis Barmpoutis; Nikos Grammalidis; Kosmas Dimitropoulos;
Open Access
  • Published: 21 Feb 2020
Abstract
Abstract Background and objective 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 t...
Subjects
free text keywords: Signal Processing, Health Informatics, Euclidean geometry, Grassmannian, Computer science, Pattern recognition, Hilbert space, symbols.namesake, symbols, Reproducing kernel Hilbert space, Tensor, Heart abnormality, Concatenation, Encoding (memory), Artificial intelligence, business.industry, business
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Funded by
EC| i-PROGNOSIS
Project
i-PROGNOSIS
Intelligent Parkinson eaRly detectiOn Guiding NOvel Supportive InterventionS
  • Funder: European Commission (EC)
  • Project Code: 690494
  • Funding stream: H2020 | RIA
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Article . 2020
Provider: ZENODO
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Other literature type . 2020
Provider: Datacite
Zenodo
Other literature type . 2020
Provider: Datacite
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