Key result
The proposed QRS complex detection algorithm using Hilbert transform achieved a sensitivity of 99.89% and a positive predictivity of 99.93% for R peak detection.
The proposed Hilbert transform-based algorithm provides highly accurate detection of QRS complexes and R peaks in ECG signals.
May aid automated ECG monitoring; leaves open independent clinical validation before practice adoption.
The present paper proposes an accurate detection algorithm of QRS complex of ECG signal. Here, Hilbert Transform of the first derivative of the ECG signal is computed at first. In the transformed signal, samples having amplitudes within a certain threshold are marked. In the original ECG signal, locations, where those marked samples undergo slope reversals, are identified as the R-peaks. Q, S, QRS onset and offset points are also detected properly. A good degree of accuracy is achieved in Sensitivity (Se = 99.89%) and Positive Predictivity (PP = 99.93%) of R peak detection. R peak height and QRS duration measurement errors are also less.
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Mukhopadhyay et al. (2012) studied ECG signal analysis. QRS complex detection algorithm using Hilbert transform, variable threshold, and slope reversal was evaluated on R peak detection accuracy (Sensitivity and Positive Predictivity). The proposed QRS complex detection algorithm using Hilbert transform achieved a sensitivity of 99.89% and a positive predictivity of 99.93% for R peak detection.
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