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The Feature Extraction of ECG Signal in Myocardial Infarction Patients

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Advances in Natural Language Processing, Intelligent Informatics and Smart Technology (SNLP 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 684))

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Abstract

Cardiovascular disease is one of the most serious diseases in the world. The physicians use electrocardiogram (ECG) to detect and diagnosis cardiovascular diseases. To enhance the ECG analysis performance, quality of the ECG signal needs to be improved. This paper proposes an ECG analysis algorithm using Wavelet transform to classify Myocardial Infarction patients. Steps in the ECG signal analysis are noise elimination of ECG signal, R peak Detection, QRS Complex Detection and Myocardial Infarction Classification. The results showed that the accuracy of classification equaled 75%, the sensitivity was 80% and the specificity was at 77.78%.

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Acknowledgements

This research was funded by King Mongkut’s University of Technology North Bangkok. Contract no. KMUTNB-GOV-59-50.

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Authors and Affiliations

  1. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Mahasak Ketcham & Anchana Muankid

Authors
  1. Mahasak Ketcham
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  2. Anchana Muankid
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Corresponding author

Correspondence to Anchana Muankid .

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Editors and Affiliations

  1. School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology (SIIT), Thammasat University, Khlong Luang, Pathum Thani, Thailand

    Thanaruk Theeramunkong

  2. Faculty of Science and Technology, Department of Computer Science, Thammasat University, Khlong Luang, Pathum Thani, Thailand

    Rachada Kongkachandra

  3. Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, Khlong Luang, Pathum Thani, Thailand

    Thepchai Supnithi

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Ketcham, M., Muankid, A. (2018). The Feature Extraction of ECG Signal in Myocardial Infarction Patients. In: Theeramunkong, T., Kongkachandra, R., Supnithi, T. (eds) Advances in Natural Language Processing, Intelligent Informatics and Smart Technology. SNLP 2016. Advances in Intelligent Systems and Computing, vol 684. Springer, Cham. https://doi.org/10.1007/978-3-319-70016-8_14

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  • DOI: https://doi.org/10.1007/978-3-319-70016-8_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70015-1

  • Online ISBN: 978-3-319-70016-8

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