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

Mohamad Sabri Bin Sinal, Eiji Kamioka

Research output: ResearchConference 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.

LanguageEnglish
Title of host publicationSmart Innovation, Systems and Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Pages161-171
Number of pages11
Volume45
ISBN (Print)9783319230238
DOIs
StatePublished - 2016
Event3rd KES International Conference on Innovation in Medicine and Healthcare, InMed 2015 - Kyoto, Japan
Duration: 2015 Sep 112015 Sep 12

Publication series

NameSmart Innovation, Systems and Technologies
Volume45
ISSN (Print)21903018
ISSN (Electronic)21903026

Other

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

Fingerprint

Computational intelligence
Artificial neural network
Electrocardiography
Artificial intelligence
Neural networks
Performance evaluation
Data base
Frequency domain
Feature extraction
Atrial fibrillation
Decision making
Logic

Keywords

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

ASJC Scopus subject areas

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

Cite this

Sinal, M. S. B., & Kamioka, E. (2016). Diagnosis of ECG data for detecting cardiac disorder using DP-matching and artificial neural network. In Smart Innovation, Systems and Technologies (Vol. 45, pp. 161-171). (Smart Innovation, Systems and Technologies; Vol. 45). Springer Science and Business Media Deutschland GmbH. DOI: 10.1007/978-3-319-23024-5_15

Diagnosis of ECG data for detecting cardiac disorder using DP-matching and artificial neural network. / Sinal, Mohamad Sabri Bin; Kamioka, Eiji.

Smart Innovation, Systems and Technologies. Vol. 45 Springer Science and Business Media Deutschland GmbH, 2016. p. 161-171 (Smart Innovation, Systems and Technologies; Vol. 45).

Research output: ResearchConference contribution

Sinal, MSB & Kamioka, E 2016, Diagnosis of ECG data for detecting cardiac disorder using DP-matching and artificial neural network. in Smart Innovation, Systems and Technologies. vol. 45, Smart Innovation, Systems and Technologies, vol. 45, Springer Science and Business Media Deutschland GmbH, pp. 161-171, 3rd KES International Conference on Innovation in Medicine and Healthcare, InMed 2015, Kyoto, Japan, 15/9/11. DOI: 10.1007/978-3-319-23024-5_15
Sinal MSB, Kamioka E. Diagnosis of ECG data for detecting cardiac disorder using DP-matching and artificial neural network. In Smart Innovation, Systems and Technologies. Vol. 45. Springer Science and Business Media Deutschland GmbH. 2016. p. 161-171. (Smart Innovation, Systems and Technologies). Available from, DOI: 10.1007/978-3-319-23024-5_15
Sinal, Mohamad Sabri Bin ; Kamioka, Eiji. / Diagnosis of ECG data for detecting cardiac disorder using DP-matching and artificial neural network. Smart Innovation, Systems and Technologies. Vol. 45 Springer Science and Business Media Deutschland GmbH, 2016. pp. 161-171 (Smart Innovation, Systems and Technologies).
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