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Myocardial Infarction Classification by Morphological Feature Extraction from Big 12-Lead ECG Data

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Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8643))

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Abstract

Rapid and accurate diagnosis of patients with acute myocardial infarction is vital. The ST segment in Electrocardiography (ECG) represents the change of electric potential during the period from the end of ventricular depolarization to the beginning of repolarization and plays an important role in the detection of myocardial infarction. However, ECG monitoring generates big volumes of data and the underlying complexity must be extracted by a combination of methods. This study combines the advantages of polynomial approximation and principal component analysis. The proposed approach is stable for the 12-lead ECG data collected from the PTB database and achieves an accuracy of 98.07 %.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan

    Julia Tzu-Ya Weng

  2. Department of Information Management, Yuan Ze University, Taoyuan, Taiwan

    Jyun-Jie Lin & Pei-Chann Chang

  3. Department of Computer Science and Information Engineering, Tamkang University, Tamsui, New Taipei City, Taiwan

    Yi-Cheng Chen

Authors
  1. Julia Tzu-Ya Weng
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  2. Jyun-Jie Lin
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  3. Yi-Cheng Chen
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  4. Pei-Chann Chang
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Corresponding author

Correspondence to Julia Tzu-Ya Weng .

Editor information

Editors and Affiliations

  1. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  2. Google Research, Mountain View, California, USA

    Haixun Wang

  3. University of Melbourne, Melbourne, Victoria, Australia

    James Bailey

  4. National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  5. Japan Advanced Institute of Science and Technology, Nomi City, Japan

    Tu Bao Ho

  6. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  7. National Chengchi University, Taipei, Taiwan

    Arbee L.P. Chen

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© 2014 Springer International Publishing Switzerland

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Weng, J.TY., Lin, JJ., Chen, YC., Chang, PC. (2014). Myocardial Infarction Classification by Morphological Feature Extraction from Big 12-Lead ECG Data. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_61

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  • DOI: https://doi.org/10.1007/978-3-319-13186-3_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13185-6

  • Online ISBN: 978-3-319-13186-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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