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

Mohamad Sabri Bin Sinal, Eiji Kamioka

研究成果: Conference contribution

抜粋

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.

元の言語English
ホスト出版物のタイトルSmart Innovation, Systems and Technologies
出版者Springer Science and Business Media Deutschland GmbH
ページ161-171
ページ数11
45
ISBN(印刷物)9783319230238
DOI
出版物ステータスPublished - 2016
イベント3rd KES International Conference on Innovation in Medicine and Healthcare, InMed 2015 - Kyoto, Japan
継続期間: 2015 9 112015 9 12

出版物シリーズ

名前Smart Innovation, Systems and Technologies
45
ISSN(印刷物)21903018
ISSN(電子版)21903026

Other

Other3rd KES International Conference on Innovation in Medicine and Healthcare, InMed 2015
Japan
Kyoto
期間15/9/1115/9/12

ASJC Scopus subject areas

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

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  • これを引用

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