Key result
The proposed ECG denoising method based on Hilbert transform and synchronous detection achieved higher signal-to-error ratios and lower mean squared errors compared to benchmark methods.
Why the study?
Does an ECG signal denoising method based on synchronous detection and Hilbert transform improve signal quality metrics compared to other denoising methods in ECG signals?
Does an ECG signal denoising method based on synchronous detection and Hilbert transform improve signal quality metrics compared to other denoising methods in ECG signals?
The proposed denoising method based on synchronous detection and Hilbert transform effectively suppresses random noises from ECG signals while preserving morphological characteristics better than several existing methods.
May improve ECG signal quality in monitoring; leaves open clinical validation before adoption.
An efficient method for Electrocardiogram (ECG) signal denoising based on synchronous detection and Hilbert transform techniques is presented. The goal of the method is to decompose a noisy ECG signal into two components classified according to their energy: (1) component with high energy representing the dominant component which is the clean ECG signal, and (2) component with low energy representing the sub-dominant component which is the contaminant noise. The investigated approach is validated through out some experimentations on MIT-BIH ECG database. Experimental results show that random noises can be effectively suppressed from ECG signals.
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Ouali et al. (2020) studied ECG signal noise. Hilbert transform and synchronous detection vs. Wavelet based method, EMD partial reconstruction, CEEMDAN plus wavelet threshold, MP-EKF, EKS, EKF was evaluated on Signal-to-error-ratio (SER), Mean squared error (MSE), and Multi-Scale Entropy Based Weighted Distortion Measure (MSEWPRD). The proposed ECG denoising method based on Hilbert transform and synchronous detection achieved higher signal-to-error ratios and lower mean squared errors compared to benchmark methods.
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