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Estimating PSD Characteristics of ECG in Comparison Between Normal and Supraventricular Subjects

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Trends in Artificial Intelligence: PRICAI 2016 Workshops (PRICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10004))

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Abstract

The aims of project are to develop an arithmetic program that can detect irregularity in electrocardiogram (ECG) and classify between two groups of normal and supraventricular ECG waveforms by using Auto regressive (AR) estimators with various model orders starting from 3rd to 9th. All AR estimators are associated with the PSD of ECG waveforms collected from a group of 30 subjects at 200 Hz sampling frequency. The best classification scores found on the 5th-order AR model are 95.99% and 72.17% obtained from training and testing the C4_5 classifier with the fifth-order coefficients. By classifying the 7th-order AR coefficients with Linear Least Squared (LS) classifier the accurate scores of 86.43% and 80.85% were obtained from training and testing cases respectively. These performance accuracies show that the proposed method is highly effective in parameterizing and classifying PSD feature as quantitative measure that can characterize the ECG signals of normal and supraventricular cardiac conditions.

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Authors and Affiliations

  1. Media Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Thaweesak Yingthawornsuk, Siriphan Phetnuam, Saowaros Singkhal & Waraporn Pattarason

Authors
  1. Thaweesak Yingthawornsuk
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  2. Siriphan Phetnuam
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  3. Saowaros Singkhal
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  4. Waraporn Pattarason
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Corresponding author

Correspondence to Thaweesak Yingthawornsuk .

Editor information

Editors and Affiliations

  1. ISIR, Osaka University, Ibaraki, Osaka, Japan

    Masayuki Numao

  2. Thammasat University, Bangkadi Muang Pathumthani, Thailand

    Thanaruk Theeramunkong

  3. National Electronics and Computer Technology Center, Language and Semantic Technology Lab, Pathum Thani, Thailand

    Thepchai Supnithi

  4. Information Technology, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

    Mahasak Ketcham

  5. Mahidol University, Bangkok, Thailand

    Narit Hnoohom

  6. Media Technology, King Mongkut's University of Technology Thonburi, Thonburi, Thailand

    Patiyuth Pramkeaw

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Yingthawornsuk, T., Phetnuam, S., Singkhal, S., Pattarason, W. (2017). Estimating PSD Characteristics of ECG in Comparison Between Normal and Supraventricular Subjects. In: Numao, M., Theeramunkong, T., Supnithi, T., Ketcham, M., Hnoohom, N., Pramkeaw, P. (eds) Trends in Artificial Intelligence: PRICAI 2016 Workshops. PRICAI 2016. Lecture Notes in Computer Science(), vol 10004. Springer, Cham. https://doi.org/10.1007/978-3-319-60675-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-60675-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60674-3

  • Online ISBN: 978-3-319-60675-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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