Key result
Undecimated wavelet transform and discrete wavelet transform using various mother wavelets were analyzed for ECG signal de-noising performance.
Why the study?
Because ECG signals are overlapped with noise from muscles, movement, electrode skin contact, breathing, and electronics, effective de-noising stages are required.
This study analyzes and compares different wavelet-based techniques for de-noising ECG signals.
Electrocardiogram (ECG) is an important biomedical signal for diagnosing heart diseases, but now it can have different other applications, such as using it as a stress recognition biomarker. Taking into account that the ECG signal is always overlapped with noise generated by muscles, body movement, electrodes skin contact, breathing and electronics, a de-noising stage must be added. This research paper analyzes and compares the noise removal by using different decomposition levels of undecimated wavelet transform (UWT) and discrete wavelet transform (DWT) based on different types of mother wavelets like orthogonal (Haar, Daubechies, Coiflets, Symmlet) and biorthogonal, whereas the other artifacts that are low frequency carriers have been already removed from the signal. During the analysis phase for each of the de-noising methods, several parameters are modified to check the accuracy and performance in a more elaborated way. The raw ECG data used in the study has been obtained by using a proposed data acquisition system.
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Iftode et al. (2020) studied this question. Undecimated wavelet transform (UWT) and discrete wavelet transform (DWT) was evaluated on Noise removal accuracy and performance. Undecimated wavelet transform and discrete wavelet transform using various mother wavelets were analyzed for ECG signal de-noising performance.
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