Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Fuzzy Unordered Rule Induction for evaluating cardiac Arrhythmia

Fuzzy Unordered Rule Induction for evaluating cardiac Arrhythmia Heart rate variations reveal current or impending heart/cardiac diseases. A non-stationary signal — heart rate — is measured using Electrocardiogram (ECG) to assess cardiac Arrhythmia. But studying ECG reports is both tedious and time consuming when you have to locate abnormalities from the collected data. This is overcome by computer based analytical tools for in-depth data study/classification for diagnosis. Automatic arrhythmia assessment is easy due to the existence of image processing techniques. Many algorithms exist for ECG signals detection/classification. This paper investigates RR interval based ECG classification procedures for arrhythmic beat classification, a process based on RR interval beat extraction using Symlet on ECG data. Extracted RR data is used as a classification feature with beats being classified through a boosting algorithm and Fuzzy Unordered Rule Induction Algorithm (FURIA). Classification efficiency evaluation was through the MITBIH arrhythmia database. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biomedical Engineering Letters Springer Journals

Fuzzy Unordered Rule Induction for evaluating cardiac Arrhythmia

Biomedical Engineering Letters , Volume 3 (2) – Aug 1, 2013

Loading next page...
 
/lp/springer-journals/fuzzy-unordered-rule-induction-for-evaluating-cardiac-arrhythmia-IHRwg0t4Tx

References (25)

Publisher
Springer Journals
Copyright
Copyright © 2013 by Korean Society of Medical and Biological Engineering and Springer
Subject
Engineering; Biomedical Engineering; Biophysics and Biological Physics; Biomedicine general; Medical and Radiation Physics
ISSN
2093-9868
eISSN
2093-985X
DOI
10.1007/s13534-013-0096-9
Publisher site
See Article on Publisher Site

Abstract

Heart rate variations reveal current or impending heart/cardiac diseases. A non-stationary signal — heart rate — is measured using Electrocardiogram (ECG) to assess cardiac Arrhythmia. But studying ECG reports is both tedious and time consuming when you have to locate abnormalities from the collected data. This is overcome by computer based analytical tools for in-depth data study/classification for diagnosis. Automatic arrhythmia assessment is easy due to the existence of image processing techniques. Many algorithms exist for ECG signals detection/classification. This paper investigates RR interval based ECG classification procedures for arrhythmic beat classification, a process based on RR interval beat extraction using Symlet on ECG data. Extracted RR data is used as a classification feature with beats being classified through a boosting algorithm and Fuzzy Unordered Rule Induction Algorithm (FURIA). Classification efficiency evaluation was through the MITBIH arrhythmia database.

Journal

Biomedical Engineering LettersSpringer Journals

Published: Aug 1, 2013

There are no references for this article.