Automated QRS Detection using Empirical Mode Decomposition and K-Means


Authors : Dr. S. S. Mehta; Shubhi Kulshrestha

Volume/Issue : Volume 8 - 2023, Issue 2 - February

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3XLomP1

DOI : https://doi.org/10.5281/zenodo.7655902

This paper proposes an algorithm using Empirical Mode Decomposition (EMD) and k-means for the detection of QRS complexes present in the ECG signal. EMD is an innovative method for decomposing any time varying, nonlinear and non-stationerysignal into a set of intrinsic mode functions (IMF). This automated algorithm is applied to the filtered ECG signal for its decomposition into its intrinsic components and further its classification is done using k-means classifier. Dataset-3 of the CSE multi-lead measurement library is used for validating the performance of the algorithm. Detection rate of the proposed algorithm came out to be 99.42% with sensitivity (Se) and prediction (+P) rates being 99.39% and 99.93% respectively. The performance of this algorithm is quite satisfactory amongst many algorithms used for the automated detection of QRS complexes

Keywords : Empirical Mode decomposition, K-means, ECG signal, QRS complex

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