Key result
The proposed automatic QRS detection method using stationary wavelet transform and Shannon energy achieved an accuracy of 99.50%, sensitivity of 99.69%, and positive predictivity of 99.81%.
Why the study?
Existing QRS detection methods using amplitude- and duration-dependent thresholds may fail in practice for QRS complexes with irregular rates and long pauses.
Does the proposed SWT and Shannon energy-based method improve QRS detection accuracy in ECG signals compared to existing methods?
Does the proposed SWT and Shannon energy-based method improve QRS detection accuracy in ECG signals compared to existing methods?
The proposed SWT and Shannon energy-based QRS detection method achieves high accuracy, sensitivity, and positive predictivity on the MIT-BIH database, outperforming existing threshold-based methods.
Advances QRS detection benchmarks on MIT-BIH; leaves open clinical translation pending prospective validation.
In this paper, we present a straightforward automatic QRS complex detection method for electrocardiogram (ECG) signal analysis applications. The proposed method consists of stationary wavelet transform (SWT) for suppressing low- and high-frequency noises and extracting QRS complexes, amplitude thresholding to suppress the effect residual noise components, Shannon energy based peak amplitude normalization, negative zero-crossing for detecting peaks candidate smoothed QRS complex waveform and peak correction for determining true R peaks in the ECG signal. On the standard MIT-BIH database, our method had an accuracy of 99.50%, sensitivity of 99.69%, and a positive predictivity of 99.81 %. The proposed method outperforms other existing methods which included sets of amplitude-and duration-dependent thresholds to include or reject missed R peaks and noise peaks, respectively that may not work in practise for the case of QRS complex with irregular rates and long-pause between two consecutive QRS complexes.
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Varghese et al. (2022) studied ECG signal analysis. Automatic QRS complex detection method using SWT and Shannon energy vs. Existing methods was evaluated on QRS detection accuracy. The proposed automatic QRS detection method using stationary wavelet transform and Shannon energy achieved an accuracy of 99.50%, sensitivity of 99.69%, and positive predictivity of 99.81%.
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