Cancellation of high-frequency noise in ECG signals using adaptive filter without external reference

Chang, Che-Cheng; Ko, Huei Chen; Chang, Kuei-Hu

doi:10.1109/bmei.2010.5639953

Cited by 15 publications

(9 citation statements)

References 8 publications

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“…An ECG signal, i.e., x (n) is transmitted through wearable wireless sensors. During the data acquisition and signal transmission, three different types of noise signals, i.e., Electromyogram (EMG), Baseline Wander (BW) and Power line interference (PLI) is mixed up with the ECG signal [4].…”

Section: Methodology Of Researchmentioning

confidence: 99%

“…When principal convergence is attained, the algorithm goes into the extraction stage. Θ(u)= log2 Ɩu/αƖ (13) At convergence stage, α is always less than 1 and denotes the power of two values. The term u/α is amplifies the ECG signal to integer type by performing shifting operation.…”

Section: Proposed Modified Normalized Least Mean Square Algorithm (Mnmentioning

confidence: 99%

“…In [3], have used several simple and efficient sign based normalized adaptive filters for the cancellation of artifacts in ECG, which are computationally superior having multipier free for update loops. The authors in [4], compared the performance of adaptive filter with and without reference noise, adaptive filter without reference noise has better performance since the noise in the primary signal may or may not correlate with the reference input to the adaptive filter.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artifacts Removal from ECG Signal Using a Multistage MNLMS Adaptive Algorithm

Mehmood¹,

Baig²,

Ehtasham-ul-Haq³

et al. 2017

IJSIP

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Section: Methodology Of Researchmentioning

confidence: 99%

Section: Proposed Modified Normalized Least Mean Square Algorithm (Mnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artifacts Removal from ECG Signal Using a Multistage MNLMS Adaptive Algorithm

Mehmood¹,

Baig²,

Ehtasham-ul-Haq³

et al. 2017

IJSIP

View full text Add to dashboard Cite

show abstract

“…Furthermore, the inability to successfully use Fourier Transforms on an ECG signal due to its non-stationary and non-linear properties illustrates the uncertainty of the exact frequency cut-offs needed for a window-based filter. In [4][5] [6], adaptive filters (LMS, NLMS, BLMS, and so on) were proposed as an upgrade over window-based filters. Active signal monitoring and noise prediction based on the discrepancy between predicted and observable signals are examples of these techniques.…”

Section: Literature Surveymentioning

confidence: 99%

ECG Denoising Using Artificial Neural Networks and Complete Ensemble Empirical Mode Decomposition

Birok¹

2021

TURCOMAT

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Electrocardiogram (ECG) is a documentation of the electrical activities of the heart. It is used to identify a number of cardiac faults such as arrhythmias, AF etc. Quite often the ECG gets corrupted by various kinds of artifacts, thus in order to gain correct information from them, they must first be denoised. This paper presents a novel approach for the filtering of low frequency artifacts of ECG signals by using Complete Ensemble Empirical Mode Decomposition (CEED) and Neural Networks, which removes most of the constituent noise while assuring no loss of information in terms of the morphology of the ECG signal. The contribution of the method lies in the fact that it combines the advantages of both EEMD and ANN. The use of CEEMD ensures that the Neural Network does not get over fitted. It also significantly helps in building better predictors at individual frequency levels. The proposed method is compared with other state-of-the-art methods in terms of Mean Square Error (MSE), Signal to Noise Ratio (SNR) and Correlation Coefficient. The results show that the proposed method has better performance as compared to other state-of-the-art methods for low frequency artifacts removal from EEG.

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“…Initially window based filters have been used for the removal of this artefact [8], but the ripples are introduced in pass and stop bands, leading errors of morphology. Adaptive algorithms [9][10][11][12] are found to not be very effective for noise cancellation. This is due to the fact that the benchmark signal is not correlated certainly with the noise components present in the fundamental input.…”

Section: Introductionmentioning

confidence: 99%

Baseline Wander Correction and Impulse Noise Suppression Using Cascaded Empirical Mode Decomposition and Improved Morphological Algorithm

Das¹,

Halder²

2017

International Journal of Applied Engineering Research

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When the cardiac impulse passes through the heart, electrical current also spreads from the heart into the adjacent tissues surrounding the heart. A small portion of the current spreads all the way to the surface of the body. If electrodes are placed on the skin on opposite sides of the heart, electrical potentials generated by the current can be recorded; the recording is known as an electrocardiogram. It is used for diagnosis of a number of cardiac diseases and disorders. But the ECG signals are often corrupted by noises and artefacts. For getting the proper information from ECG, it first must be de-noised. ECG signal is dominated mainly by high frequency impulse noises and baseline wander. These noise must be removed for better clinical assessment. In this paper, we propose an improve ECG intensification method based on empirical mode decomposition and improved morphological algorithm. The method is examined through experimental simulations on MIT-BIH databases. Quantitative and qualitative results show that the proposed EMD based method caters improved results for de-noising and baseline wander removal.

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Cancellation of high-frequency noise in ECG signals using adaptive filter without external reference

Cited by 15 publications

References 8 publications

Artifacts Removal from ECG Signal Using a Multistage MNLMS Adaptive Algorithm

Artifacts Removal from ECG Signal Using a Multistage MNLMS Adaptive Algorithm

ECG Denoising Using Artificial Neural Networks and Complete Ensemble Empirical Mode Decomposition

Baseline Wander Correction and Impulse Noise Suppression Using Cascaded Empirical Mode Decomposition and Improved Morphological Algorithm

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