Correlation-based ECG Artifact Correction from Single Channel EEG using Modified Variational Mode Decomposition

Dora, Chinmayee; Biswal, Pradyut Kumar

doi:10.1016/j.cmpb.2019.105092

Cited by 22 publications

(6 citation statements)

References 31 publications

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“…where y[l] andŷ[l] represent original clean ECG signal and reconstructed ECG signal, respectively. In most ECG compression algorithms, the percentage rootmean-square differences (P RD) measure is employed [13] and it can be defined as:…”

Section: Simulated Signalsmentioning

confidence: 99%

“…For example, the high-pass filter [8], nonlinear filter bank [9], median filter and morphology adaptive filter [10], adaptive switching mean filter (ASMF) [11]. Also, other signal processing methods like eigenvalue decomposition [12], Modified Variational Mode Decomposition [13], wavelet transform [15], and Empirical Mode Decomposition [14] can still realize the removal of baseline wander by decomposing the baseline wander and other ECG signals into different components. Powerline (AC) interference is a high-frequency additive noise of 50 or 60 Hz.…”

Section: Introductionmentioning

confidence: 99%

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A novel method based on Adaptive Periodic Segment Matrix and Singular Value Decomposition for removing EMG artifact in ECG signal

Chen

Lin

Huang³

et al. 2019

Preprint

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Background: The Electrocardiogram (ECG) signals are usually used to detect and monitor human health. However, the Electromyogram (EMG) artifact also can be obtained during measurement, these make difficult for doctors in correct diagnosis. In general, the ECG signal is periodic while EMG artifact is non-stationary and has overlapped with it under the frequency domain. According to these characteristics, it is necessary to extract clean ECG signals from noisy EMG artifact signals by using the periodic separation method. Method: A novel Adaptive Periodic Segment Matrix (APSM) based on Singular Value Decomposition (SVD) is proposed for extracting clean ECG from EMG artifact. Firstly a periodic segment estimation method is proposed by obtaining an average periodic length and RR intervals constraint via envelope spectrum of the measured signal. Secondly, the R wave peaks and its position of the ECG signals are detected by these. After that, APSM with rank one is formed using R wave peaks and the calculated RR intervals constraint, Then SVD is processed on this matrix, the restructured ECG signals will be obtained by the first maximum singular value of the formed matrix. The validation of proposed method is made by applying the algorithm to ECG records from the MIT-BIH Arrhythmia Database. The zero-mean percent root-mean-square difference (PRD 1 ), Cross-correlation coefficient and output signal to noise ratio (SNR output ) have been calculated for presenting the algorithm performance by comparison with other methods. Finally two heart disease cases have been studied for P wave and ST segment detection under noisy ECG with EMG artifact.Results: The proposed methods achieved significant improvement in output signal-to-noise ratio, percentage root-mean-square differences and lead to the higher value of cross-correlation coefficient between the original (clean) ECG and the denoised ECG signal. Also, the reconstructed ECG signal can be better able to follow the trend of original (clean) ECG signal under the EMG noise. Conclusion: The proposed periodic segment estimation method can adaptively find the periodic length in ECG signal by using envelope spectrum. Also, the more strict rank one trajectory matrix has been formed in APSM by using R wave peaks and RR intervals constraint. The results show that the proposed APSM-SVD method is effective for EMG artifact removal and extracting the clean ECG signal. The R peak, P wave, QRS complex and ST segment can be preserved in the reconstructed ECG signal.

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Section: Simulated Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel method based on Adaptive Periodic Segment Matrix and Singular Value Decomposition for removing EMG artifact in ECG signal

Chen

Lin

Huang³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…In the study by Jiang, a multi-dictionary based sparse representation approach is proposed for automatic detection of epileptic EEG spikes (Jiang et al, 2020 ). Dora applied variational mode decomposition for correcting artifacts in EEG measurements (Dora and Biswal, 2020 ). Chen proposed a sparse Fourier transform and applied it in power-line artifact removal (Chen et al, 2021b ).…”

Section: Introductionmentioning

confidence: 99%

Potential of Overcomplete Wavelet Frame Expansion for Facilitating Electroencephalogram Information Mining

et al. 2022

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“…Electroencephalogram (EEG) is a voltage test recording the electrical activity of the neurons in the brain. EEG is obtained in various frequencies including delta (1-4 Hz), theta (4-8 Hz), alpha , beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (greater than 30 Hz) [1]. EEG brain rhythms with different frequency ranges are shown in Table 1 [2].…”

Section: Introductionmentioning

confidence: 99%

“…Their proposed method worked much robust in the low sampling rate based on the benchmark and real data. Dora and Biswal[26] have suggested an ECG artifact correction algorithm for the automatic diagnosis and analysis of single-channel EEG signals. It employed a modified and enhanced signal decomposition version for attaining band limited intrinsic mode functions (BLIMFs).…”

mentioning

confidence: 99%

Artifact Removal Methods in EEG Recordings: A Review

Mathe

Padmaja

Krishna

2021

Proc. eng. technol. innov.

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To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods.

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Correlation-based ECG Artifact Correction from Single Channel EEG using Modified Variational Mode Decomposition

Cited by 22 publications

References 31 publications

A novel method based on Adaptive Periodic Segment Matrix and Singular Value Decomposition for removing EMG artifact in ECG signal

A novel method based on Adaptive Periodic Segment Matrix and Singular Value Decomposition for removing EMG artifact in ECG signal

Potential of Overcomplete Wavelet Frame Expansion for Facilitating Electroencephalogram Information Mining

Artifact Removal Methods in EEG Recordings: A Review

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