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PCA as an effective tool for the detection of R-peaks in an ECG signal processing

Author

Listed:
  • Varun Gupta

    (KIET Group of Institutions)

  • Nitin Kumar Saxena

    (KIET Group of Institutions)

  • Abhas Kanungo

    (KIET Group of Institutions)

  • Parvin Kumar

    (KIET Group of Institutions)

  • Sourav Diwania

    (KIET Group of Institutions)

Abstract

Visual inspection of R-peaks in Electrocardiogram (ECG) signal is avoided because of limited resolution and in the variation of parameters of the underlying subject (patient). Therefore, Principal Component Analysis has been used for R-peak detection without any pre-processing in noisy and different morphologies of the ECG signal with fractional Fourier transform which is using as a feature extraction technique. For validating this research work MIT-BIH Arrhythmia database is considered. The performance of developed algorithm is judged based on sensitivity, positive predictive value and accuracy. It has been revealed that the developed algorithm is capable to analyze ECG signals in both situations either normal or abnormal. It opens their huge applications in the time varying signals which can capture important clinical attributes in critical pathological situations.

Suggested Citation

  • Varun Gupta & Nitin Kumar Saxena & Abhas Kanungo & Parvin Kumar & Sourav Diwania, 2022. "PCA as an effective tool for the detection of R-peaks in an ECG signal processing," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2391-2403, October.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:5:d:10.1007_s13198-022-01650-0
    DOI: 10.1007/s13198-022-01650-0
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    References listed on IDEAS

    as
    1. Qiong Yu & Aili Liu & Tiebing Liu & Yuwei Mao & Wei Chen & Hongxing Liu, 2019. "ECG R-wave peaks marking with simultaneously recorded continuous blood pressure," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-17, March.
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    Cited by:

    1. Chhaya Dubey & Dharmendra Kumar & Ashutosh Kumar Singh & Vijay Kumar Dwivedi, 2024. "Applying machine learning models on blockchain platform selection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 3643-3656, August.

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