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K-nearest neighbour-based algorithm for P- and T-waves detection and delineation.

Indu Saini1, Dilbag Singh, Arun Khosla

  • 1Dr B R Ambedkar National Institute of Technology Jalandhar , Jalandhar , India.

Journal of Medical Engineering & Technology
|February 11, 2014
PubMed
Summary

This study introduces a K-Nearest Neighbour (KNN) algorithm for automated Electrocardiogram (ECG) delineation, accurately identifying key waveform points and boundaries. The KNN approach demonstrates high accuracy, aiding reliable ECG analysis.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiology

Background:

  • Automated Electrocardiogram (ECG) delineation is crucial for reliable waveform analysis.
  • Accurate detection of QRS-complex, P-wave, and T-wave fiducial points and boundaries is essential for clinical interpretation.

Purpose of the Study:

  • To propose and evaluate a K-Nearest Neighbour (KNN) algorithm for automated ECG delineation.
  • To accurately locate fiducial points and waveform boundaries of QRS-complex, P-wave, and T-wave in ECG signals.

Main Methods:

  • Utilized the K-Nearest Neighbour (KNN) statistical pattern recognition algorithm.
  • Employed ECG signal gradients for feature extraction.
  • Detected QRS-complex, P-wave, and T-wave fiducial points and boundaries sequentially.

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Main Results:

  • Achieved 92.8% accuracy on the CSE database, showing high agreement with manual annotations.
  • Delineation results on CSE and QT databases were compared against accepted tolerances.
  • Computed key ECG parameters including QRS duration, heart rate, and QT-interval using the KNN algorithm on BIOPAC®MP100 data.

Conclusions:

  • The proposed KNN algorithm offers high accuracy and stability for automated ECG delineation.
  • The method reliably identifies fiducial points and waveform boundaries, supporting clinical diagnostics.
  • KNN demonstrates effectiveness in analyzing ECG signals for various cardiac parameters.