Diagnosis of ECG data for detecting cardiac disorder using DP-matching and artificial neural network

Mohamad Sabri Bin Sinal, Eiji Kamioka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Computational Intelligence has made a huge impact on solving many complicated problem particularly in the medical field. With the advancement of computational intelligence where the effectiveness of data analysis is at high stake, the process of classifying and interpreting data accurately based on logical reasoning in decision making is not a big issue. This study discusses the process of diagnosing cardiac disorder using computational intelligence with specific focus on the feature extraction where the attribute of identifying Normal Sinus and Atrial Fibrillation rhythms using Physionet.org database is examined. In this paper, an algorithm to diagnose the cardiac disorder based on DP-Matching will be proposed where the time and frequency domains of ECG signal segments are introduced. At the end of this paper, the performance evaluations of the proposed method will be shown with the analysis by ANN.

Original languageEnglish
Title of host publicationInnovation in Medicine and Healthcare, 2015
EditorsRobert J. Howlett, Yen-Wei Chen, Satoshi Tanaka, Carlos Torro, Lakhmi C. Jain
PublisherSpringer Science and Business Media Deutschland GmbH
Pages161-171
Number of pages11
ISBN (Print)9783319230238
DOIs
Publication statusPublished - 2016
Event3rd KES International Conference on Innovation in Medicine and Healthcare, InMed 2015 - Kyoto, Japan
Duration: 2015 Sept 112015 Sept 12

Publication series

NameSmart Innovation, Systems and Technologies
Volume45
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Other

Other3rd KES International Conference on Innovation in Medicine and Healthcare, InMed 2015
Country/TerritoryJapan
CityKyoto
Period15/9/1115/9/12

Keywords

  • Artificial neural network (ANN)
  • Atrial fibrillation rhythm
  • Dynamic programming (DP-matching)
  • Electrocardiogram (ECG)
  • Normal sinus rhythm

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Computer Science(all)

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