Baseline wander removal for bioelectrical signals by quadratic variation reduction

Fasano, Antonio; Villani, Valeria

doi:10.1016/j.sigpro.2013.11.033

Cited by 46 publications

(38 citation statements)

References 24 publications

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“…These methods include Hilbert vibration decomposition (HVD), in which the highest energy component of the HVD that was the first component corresponds to the BW signal [17]. In [18] BW was removed by using a novel technique based on quadratic variation reduction. In [19] fractal modeling was used to create a projection operator that allows BW removal.…”

Section: Introductionmentioning

confidence: 99%

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Upper envelope detection of ECG signals for baseline wander correction: a pilot study

Ouali¹,

Ghanai²,

Chafaa³

2018

Turk J Elec Eng & Comp Sci

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Baseline wander (BW) is a common low frequency artifact in electrocardiogram (ECG) signals. The prime cause from which BW arises is the patient's breathing and movement. To facilitate reliable visual interpretation of the ECG and to discern particular patterns in the ECG signal, BW needs to be removed. In this paper, a novel BW removal method is presented. The hypothesis is based on the observation that ECG signal variation covaries with its BW. As such, the P, Q, R, S, and T peaks will follow the baseline drift. On this basis, the following proposition is true: a reliable approximation of the baseline drift can be obtained from the shape derived from the interpolation of one form of the ECG signal peak (peak envelope). The simulation was performed by adding artificial BW to ECG signal recordings. The signal-to-noise ratio, mean squared error, and improvement factor criteria were used to numerically evaluate the performance of the proposed approach. The technique was compared to that of the Hilbert vibration decomposition method, an empirical-mode decomposition technique and mathematical morphology. The results of the simulation indicate that the proposed technique is most effective in situations where there is a considerable distortion in the baseline wandering.

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Section: Introductionmentioning

confidence: 99%

“…In another study, the empirical mode decomposition technique was used with a low-pass filter that had been devised from the averages of opening and closing operators [8]. Many other approaches have been reported in the literature to address the problem of ECG enhancement [18,[21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Upper envelope detection of ECG signals for baseline wander correction: a pilot study

Ouali¹,

Ghanai²,

Chafaa³

2018

Turk J Elec Eng & Comp Sci

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“…In the literature, ECG signal processors operate at different frequency ranging from 0.25 Hz to 400 kHz [9]- [12]. ECG Signal detection includes elimination of different noises like baseline drift [10]- [14], waveform detection [15]- [17], feature extraction [18], and heart rate classification [19]- [28]. Among the several techniques investigated in the literature are included time domain analysis [29], statistical approach [30]- [32], hybrid features [33], [34], frequency-based analysis [35], and time-frequency analysis [36]- [38] for feature extraction of ECG signals.…”

Section: Introductionmentioning

confidence: 99%

R Wave Extraction from ECG Signal using Modified Wavelet Transform

2017

Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering

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Electro Cardiogram (ECG) is used to measure and diagnose electrical activity of heart. R peak detection from ECG signal is our main goal. It is the basic mark for identification of different arrhythmias. In this paper, R wave extraction is performed by using Wavelet Transform. The wavelet transform has risen over late years as an effective time-frequency analysis and it is efficiently analyze complex non stationary signals. In this research, R wave is extracted accurately then heart beat is analyzed by the detection of RR intervals. In this research, R peak is detected accurately by this type of approach. By accurate detection of R peak, cardiac diseases can easily be identified such as Sinus tachycardia, Sinus bradycardia, Supraventricular tachycardia (SVT), atrial fibrillation (AF), Ventricular tachycardia, Heart block and Sudden Cardiac Arrest (SCA).

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“…The wavelet transform was considered to eliminate baseline wandering in ECG by implementing a search algorithm based on computing wavelet packet coefficients [16] and a hierarchical model utilizing Independent Component Analysis (ICA) [17] was proposed to suppress the baseline wandering in the ECG. To reduce baseline wandering for bioelectrical signals, constrained convex optimization problem [18] was solved and the fractal modeling [19] was also considered by interpreting baseline fluctuation as the first order Brownian-motion process. All of the previously suggested digital filters to reduce baseline wandering face a drawback: a certain amount of time delay is required to get the filtered signal.…”

Section: Introductionmentioning

confidence: 99%

Removal of baseline wandering in ECG signal by improved detrending method

Shin

Kim

Song

et al. 2015

BME

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Abstract. The very first step to process electrocardiogram (ECG) signal is to eliminate baseline wandering interference that is usually caused by electrode-skin impedance mismatch, motion artifacts due to a patient's body moment or respiratory breathing. A new method is thus suggested to remove baseline wandering in ECG by improving the detrending method that was originally proposed for eliminating slow non-stationary trends from heart rate variability (HRV). In our proposed method, a global trend is estimated in terms of baseline wandering by merging the local trend based on an ECG segment that represents a part of the ECG signal. The experimental results show that the improved detrending method can efficiently resolve baseline wandering without distorting any morphological characteristic embedded in the ECG signal in no time delay manner.

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Baseline wander removal for bioelectrical signals by quadratic variation reduction

Cited by 46 publications

References 24 publications

Upper envelope detection of ECG signals for baseline wander correction: a pilot study

Upper envelope detection of ECG signals for baseline wander correction: a pilot study

R Wave Extraction from ECG Signal using Modified Wavelet Transform

Removal of baseline wandering in ECG signal by improved detrending method

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