A hybrid method for removal of power line interference and baseline wander in ECG signals using EMD and EWT. (May 2021)

• Record Type: Journal Article
• Authors: Boda, Somaraju; Mahadevappa, Manjunatha; Dutta, Pranab Kumar
• Is Part Of: Biomedical signal processing and control. Volume 67(2021)
• Publication Date: 2021-05
• Subjects: Electrocardiogram (ECG) -- Power line interference (PLI) -- Baseline wander (BW) -- Empirical mode decomposition (EMD) -- Ratio of the zero crossing numbers (RZCN) -- Empirical wavelet transform (EWT) -- ECG denoising; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102466
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A hybrid method based on EMD and EWT is applied for ECG signal enhancement. The relation between ratio of the zero crossing numbers of ECG signal and its power line interference presented is explored. The relation between ratio of the zero crossing numbers of ECG signal and its baseline wander presented is explored. The proposed method is tested and validated using MIT-BIH arrhythmia database ECG signals. The proposed method outperfomrs other methods based on EVD, MRLS-HVD and EMD-WT-LPF. Abstract: The removal of power line interference (PLI) and baseline wander (BW) is an essential task for the analysis of the ECG signal. This paper presents a new method to suppress PLI and BW from electrocardiogram (ECG) signals using a hybrid approach of empirical mode decomposition (EMD) and empirical wavelet transform (EWT). The proposed method consists of four stages: EMD-based ECG signal decomposition, construction of high and low-frequency sub-signals based on the ratio of the zero crossing numbers (RZCN), estimation of PLI and BW through using EWT, and the interference subtraction. The proposed method is tested and validated through experiments performed over a wide variety of real ECG signals which are available on the MIT-BIH arrhythmia database. The results show that the proposed method can effectively remove PLI and BW without distorting the ECG signal components. The efficacy of the proposed method is compared with some of the state-of-the-art methods in terms of output signal-to-noise ratio ( S N R o ), cross-correlation coefficient ( ρ ), percent root mean square difference ( P R D ), and weighted diagnostic distortion (WDD) at a different input signal-to-noise ratio ( S N R i ) . The qualitative and quantitative results show that the proposed method outperforms the existing methods.