A variational mode decomposition-based R peak detection method achieved an average sensitivity of 99.77% and a positive predictivity of 99.91% on the standard MIT-BIH arrhythmia database.
Does a variational mode decomposition-based method improve R peak detection and noise suppression in ECG signals?
A novel variational mode decomposition-based algorithm provides highly accurate R peak detection (99.77% sensitivity) and effective noise suppression in ECG signals without relying on heuristic search-back rules.
Automatic and reliable detection of R peak from electrocardiogram (ECG) signal is essential in both pathological and nonpathological applications. The presence of various kinds of noises and artifacts makes it still a challenging problem in the accurate and reliable extraction of ECG parameters in ambulatory conditions. In this article, we present a straightforward R peak detection and ECG denoising method based on the variational mode decomposition, mode selection, first-order derivative, Shannon energy (SE)-based nonlinear amplification, Hilbert transform (HT), and positive zero-crossing point. Optimal parameters were chosen for suppressing the low- and high-frequency noises and extracting the candidate QRS complex portions. The challenging effect of long P and T waves is suppressed in differentiation. The SE-based amplification process is used to magnify medium-amplitude QRS complexes largely compared to high-amplitude QRS complexes to accurately detect low-amplitude QRS complexes in the presence of high-amplitude QRS complexes. The HT is used to automatically detect the location of candidate R peaks from smoothed SE envelope by processing positive zero-crossing points of HT of the SE waveform. The R peak detection results on the standard MIT-BIH arrhythmia database showed that the method had an average sensitivity of 99.77% and a positive predictivity of 99.91%. This method is straightforward unlike existing R peak detections based on a search-back algorithm with heuristic decision rules to include missed peaks and reject noisy peaks. The denoising results showed the noise removal capability of the proposed algorithm with the preservation of local waves in the ECG signal.
Varghees et al. (Mon,) conducted a other in Arrhythmia (ECG analysis). Variational mode decomposition-based R peak detection and ECG denoising method was evaluated on R peak detection sensitivity. A variational mode decomposition-based R peak detection method achieved an average sensitivity of 99.77% and a positive predictivity of 99.91% on the standard MIT-BIH arrhythmia database.
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