Baseline normalisation of ECG signals using empirical mode decomposition and mathematical morphology

Ji, Tianyao; Lu, Ze-Qi; Wu, Q. H.; Ji, Zhen

doi:10.1049/el:20082709

Cited by 37 publications

(54 citation statements)

References 2 publications

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“…DISCUSSION Results in Table 1 indicate a significant improvement (10 dB in average) when using the removal algorithm for SNR values below 2 dB, especially for negative values. This improvement is comparable or even better than results obtained by digital filters [6], wavelets [8], [10] and mathematical morphology [11] and lower than with neural networks [13], [14]. When the initial SNR value is higher than 5 dB, the gain of using the removal algorithm is reduced, since the amount of noise present in the signal is also reduced and its power is lower than the signals.…”

Section: Resultssupporting

confidence: 48%

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Improved removal of electrocardiogram baseline wandering

et al. 2013

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Baseline wandering interference causes inaccurate detection of electrocardiogram (ECG) characteristics such as detection of the R point. This paper presents a novel phase spacebased approach for baseline wandering removal of ECG signals. The baseline wander contaminated ECG signal is transformed to the phase space by using the signal derivative. Baseline wandering was eliminated by shifting loops of the ECG phase portrait on a predefined fixed position. This resulted in signal discontinuities in time domain which were solved using linear correction. The method was evaluated on synthetized ECG signals and on signals from the MIT-BIH database. Results show an improvement of signal-to-noise ratio after baseline wandering removal in phase space, comparable with related works, and efficient elimination of time-domain discontinuities in time domain.

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Section: Resultssupporting

confidence: 48%

“…detection of characteristic points, feature determination, etc. Previous ECG baseline wandering removal procedures use approaches such as FIR and IIR filtering [5], [6], Kalman filtering [7], wavelet transform [8]- [10], mathematical morphology [11], Fourier series [12], neural networks [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Improved removal of electrocardiogram baseline wandering

et al. 2013

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“…The experimental results indicate that this method performs accurate removal (COR = 100%) of ECG BW, while only 92.2% for median filtering method and 99.6% for EMD (empirical mode decomposition) correction method [13].…”

Section: Resultsmentioning

confidence: 97%

“…To analyze and evaluate the filtering performance, we used PSD (power spectrum density) [11], percent of COR (cross-corelation coefficient) [12,13] and PRD (percent of root squared mean difference) [14] as a quantitative criteria. Level DWT coefficients 1 C1 d1 2 C20 d21 d22 d23 3 C30 d31 d32 d33 d34 d35 d36 d37 where, x(n) is the original signal without noises and y(n) is the filtered signal.…”

Section: Denoising Evaluation Criteriamentioning

confidence: 99%

Electrocardiogram Signal Denoising Using Discrete Wavelet Transform

HANINE¹,

Elhassane²,

Haddadi³

et al. 2014

CTA

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Abstract:The most common noises in ECG (electrocardiogram) signal processing are BW (baseline wandering) and the 50 or 60 Hz PLI (power line interferences). In order to remove these two major source of noises, we have used the recent powerful DWT (discrete wavelet transform) signal processing in ECG signals which are obtained from MIT-BIH Arrhythmia Database. The results indicate that DWT is a good method for filtering noises without changing the morphology of ECG, and can be applied to all types of ECG signals, whether normal or presenting arrhythmias.

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“…The starting point of EMD is to estimate a signal as a sum of low frequency and the detail components for high frequencies components are referred to as Intrinsic Mode Functions (IMFs) and the low frequency components are called residuals. The process of finding the IMF is called the shifting process [9] …”

Section: Emd Algorithmmentioning

confidence: 99%

Comparison of Some EMD based Technique for Baseline Wander Correction in Fetal ECG Signa

Ghosh¹,

Poonia²

2015

IJCA

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Baseline wander (BW) is a low frequency artifact in biomedical electronic recordings. It is usually caused by patient's respiration or movement of equipments. The removal of this artifact is important in ECG recordings for reliable visual interpretation. This paper presents the implementation of Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and EMD based method to remove this disturbance. The EMD based technique serves as an efficient method to remove baseline wander with minimum signal distortion. The results highlights the main differences among all different methods and also show that the EMD based technique is able to remove best baseline wander.

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Baseline normalisation of ECG signals using empirical mode decomposition and mathematical morphology

Cited by 37 publications

References 2 publications

Improved removal of electrocardiogram baseline wandering

Improved removal of electrocardiogram baseline wandering

Electrocardiogram Signal Denoising Using Discrete Wavelet Transform

Comparison of Some EMD based Technique for Baseline Wander Correction in Fetal ECG Signa

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