Rule-based Method for Extent and Localization of Myocardial Infarction by Extracted Features of ECG Signals using Body Surface Potential Map Data.

Naser Safdarian, Nader Jafarnia Dabanloo, Seyed Ali Matini, Ali Motie Nasrabadi

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Article in Journal of medical signals and sensors, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

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2 citing papers in PubMed.

Detailed Anatomical and Electrophysiological Models of Human Atria and Torso for the Simulation of Atrial Activation.PloS one · 2015
Article
Right ventricle functional parameters estimation in arrhythmogenic right ventricular dysplasia using a robust shape based deformable model.Journal of medical signals and sensors · 2014
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4 authors.

Naser SafdarianDepartment of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Nader Jafarnia DabanlooDepartment of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Seyed Ali MatiniInterventional and Consultant Cardiologist, Mehr General Hospital, Tehran, Iran.

Ali Motie NasrabadiBiomedical Engineering Faculty, Shahed University, Tehran, Iran.

Funding

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6 · The paper itself

Abstract

In this study, a method for determining the location and extent of myocardial infarction using Body Surface Potential Map data of PhysioNet challenge 2007 database is presented. This data is related to four patients with myocardial infarction. We used two patients as training set to determine rules and two other patients as testing set of the proposed model. First, T-wave amplitude, T-wave integral, Q-wave amplitude and R-wave amplitude as four features of ECG signals were extracted. Then we defined several rules and proper thresholds for localization and determining the extent of myocardial infarction. To determine the precise location and extent of myocardial infarction, 17-segment standard model of left ventricle was used. Finally, overall accuracy of this method was shown with SO, CED and EPD parameters. We obtained 1.16, 1 and 5.3952 for SO, CED and EPD, respectively, in our test data. Two main advantages of this method are simplicity and high accuracy.

Indexed as

Body surface potential map dataelectrocardiogram signal processingelectrocardiogram signalsfeature extractionmyocardial infarction

Identifiers

PMID24672761

PMCPMC3959003

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LicenceCC BY-NC-SA

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.