Key result
Db4 DyWT algorithm achieves ~99% R peak detection rate on real ECG data.
Why the study?
A robust method for R peak detection in ECG signals is needed to improve heart rate variability studies in both healthy and diseased subjects.
A novel R peak detection algorithm using wavelet transforms provides a robust method for ECG signal processing and heart rate variability analysis.
May enhance ECG denoising and R-wave detection in research; leaves open clinical validation before practice adoption.
We propose in this work a method of electrocardiogram (ECG) signal pretreatment by the application of Discreet Wavelet Transform DWT by automatically determining the optimal order of decomposition.After the purification of the original signal, we describe an algorithm to detect R waves based on the Dyadic Wavelet Transform DyWT by applying a windowing process.This algorithm is validated on a sample of synthesis ECG signal with and without noise which we have proposed and on real data.Finally, once the R peaks of real data are detected, we use three methods of RR intervals analysis by calculating the standard deviation of heart rate and applying the Fast Fourier Transform FFT and the Wavelet Transform on detected RR intervals to study the Heart Rate Variability (HRV).A comparative study between the analysis results of detected RR intervals in healthy and diseased subjects through the application of the FFT and the Wavelet Transform will be given.
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Nouira et al. (2013) studied Arrhythmia (n=10). Db4 DyWT algorithm with windowing and pretreatment vs. Other R peak detection algorithms (historical controls) was evaluated on R peak detection rate. The proposed Db4 DyWT algorithm achieved an R peak detection rate of 99.32% with an error rate of 0.68% on real ECG data from the MIT-BIH arrhythmia database.
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