Key result
Wavelet thresholding outperforms standard techniques for ECG de-noising with better signal-to-noise ratio and execution time.
Why the study?
An efficient and robust method for de-noising ECG signals that automatically selects the optimal mother wavelet and improves noise elimination was needed.
Does a wavelet thresholding strategy and robust shrinkage approach improve de-noising of ECG signals compared to state-of-the-art techniques?
Does a wavelet thresholding strategy and robust shrinkage approach improve de-noising of ECG signals compared to state-of-the-art techniques?
A novel wavelet thresholding strategy and robust shrinkage approach improves the de-noising of ECG signals compared to existing techniques.
May aid ECG signal clarity in clinical recordings; leaves open prospective validation for diagnostic impact.
This paper introduces an efficient wavelet thresholding strategy and robust shrinkage approach (WTS) for de‐noising the ECG signals. The optimal mother wavelet for de‐noising the ECG signal is automatically selected based on cross‐correlation and energy‐to‐entropy indices. A dynamic threshold is applied to various levels of decomposition to eliminate different types of noise. A shrinkage approach is suggested to attain seamless on–off transitions. The achievement of the WTS is compared with the state of the art reported in the literature using 180 real ECG signals collected from 60 volunteers. The ECG signals were recorded using three standard ECG machines without applying the filtering option to provide a diverse range of noisy ECG signals. The de‐noising outcomes show that the presented WTS can automatically select the optimal mother wavelet for the particular ECG signal, and it is superior to other techniques in terms of the average predicted signal‐to‐noise ratio (), the average predicted root‐mean‐square error (), and the average execution time (AET).
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Anas Fouad Ahmed (2023) studied ECG signal de-noising (n=60). Wavelet thresholding strategy and robust shrinkage approach (WTS) vs. State of the art techniques was evaluated on Average predicted signal-to-noise ratio, average predicted root-mean-square error, and average execution time. The presented wavelet thresholding strategy and robust shrinkage approach (WTS) was superior to other techniques for de-noising ECG signals in terms of signal-to-noise ratio, root-mean-square error, and execution time.
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