Wavelet based method for localization of myocardial infarction using the electrocardiogram

This paper presents detection and localization of myocardial infarction (MI) using REF neural networks classifier with wavelet coefficient as features extracted from frank leads. Detection of MI aim to classify healthy and having MI and Localization aim to specify the infracted region of the heart....

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Bibliographic Details
Published inComputing in Cardiology 2014 pp. 645 - 648
Main Authors Noorian, Azadeh, Dabanloo, Nader Jafarnia, Parvaneh, Saman
Format Conference Proceeding
LanguageEnglish
Published CCAL 01.09.2014
Subjects
Abstracts
Electrocardiography
Heart
Lead
Myocardium
Noise measurement
Vectors
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Summary:This paper presents detection and localization of myocardial infarction (MI) using REF neural networks classifier with wavelet coefficient as features extracted from frank leads. Detection of MI aim to classify healthy and having MI and Localization aim to specify the infracted region of the heart. The electrocardiogram (ECG) source used in the PTB database available on physio-bank. Frank lead vx, vy, vz is get from 12 lead ECG using Dower transformation. Wavelet coefficient of different levels and families of each beat are extracted. We extract wavelet coefficient in three level 3, 4, 5 from threewavelethaar, db4, db10 to evaluate the different kinds of wavelet.
ISBN:9781479943463
1479943460
ISSN:0276-6574
2325-8853