Comparison of Baseline Wander Removal Techniques considering the Preservation of ST Changes in the Ischemic ECG: A Simulation Study

Lenis, Gustavo; Pilia, Nicolas; Loewe, Axel; Schulze, W; Dössel, Olaf

doi:10.1155/2017/9295029

Cited by 89 publications

(65 citation statements)

References 44 publications

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“…Lenis, et al [31] compares baseline-wander removal techniques and concludes that wavelet-based baseline cancellation outperforms other methods in terms of preserving the ST-segment of ECG signal. However, it is computationally intensive as compared to Butterworth filtering or polynomial interpolation.…”

Section: Discussionmentioning

confidence: 99%

Detection and Classification of Baseline-Wander Noise in ECG Signals Using Discrete Wavelet Transform and Decision Tree Classifier

Haider¹,

Alhussein²

2019

ELEKTRON ELEKTROTECH

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An electrocardiogram (ECG) signal is usually contaminated with various noises, such as baseline-wander, power-line interference, and electromyogram (EMG) noise.Denoising must be performed to extract meaningful information from ECG signals for clinical detection of heart diseases. This work is focused on baseline-wander noise as it shares the same frequency spectrum as the ST segment of ECG signals. Hence, it is important to estimate the baseline-wander prior to its removal from ECG signals. This paper presents a method for classifying each segment of the ECG signal's baseline-wander as minimal, moderate or large. We use the C4.5 decision tree algorithm to model the classifier using the WEKA data-mining tool. We test the proposed method on ECG signals obtained from the MIT-BIH arrhythmia database (48 ECG recordings, each slightly longer than 30 min). We use 36 ECG recordings for training the classifier with the remaining 12 ECG recordings as the test data for classification. We partition each recording into 5 second, non-overlapping segments, which result in 361 segments for each record. The classification results show that the model classifier achieves an average sensitivity of 97.36 %, specificity of 99.50 %, and overall accuracy of 98.89 % in classifying the baseline-wander noise in ECG signals. The proposed method effectively addresses the question of identifying the minimal baselinewander segments. Moreover, the proposed framework may help in devising an algorithm for the selective filtering of moderate and large baseline-wander segments to achieve the best tradeoff between accuracy and computational cost.

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Section: Discussionmentioning

confidence: 99%

Detection and Classification of Baseline-Wander Noise in ECG Signals Using Discrete Wavelet Transform and Decision Tree Classifier

Haider¹,

Alhussein²

2019

ELEKTRON ELEKTROTECH

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“…The moving median filter is based on the same principle as the moving average filter, in which the median within a moving window of a given length is calculated instead of the average. The moving median filter has also been used to reduce low-frequency noise in some studies [8,36]. In our study, the window length of the moving median filter was chosen to be 361, the same length as the moving average filter, for comparison purposes.…”

Section: Moving Median Filtermentioning

confidence: 99%

“…Synthetic motion artefact-corrupted ECG signals are shown in Figures 6 and 7. 8 The adaptive filter can be implemented by different adaptive algorithms, but Least Mean Square (LMS), Normalized Least Mean Square (NLMS), and Recursive Least Square (RLS) are the three most commonly used. Every algorithm has its pros and cons.…”

Section: Experimental Signalsmentioning

confidence: 99%

“…Over the last few decades, traditional signal-processing techniques such as finite impulse response/infinite impulse response (FIR/IIR) high-pass filtering, moving average filtering, and wavelet transform have been used to reduce the motion artefact [5][6][7][8][9]. Among all these noise-reduction methods, an IIR high-pass filter with a cut-off frequency of 0.5/0.67 Hz is one of the most common methods used in practice today [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring

Stylios

2020

Sensors

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A motion artefact is a kind of noise that exists widely in wearable electrocardiogram (ECG) monitoring. Reducing motion artefact is challenging in ECG signal preprocessing because the spectrum of motion artefact usually overlaps with the very important spectral components of the ECG signal. In this paper, the performance of the finite impulse response (FIR) filter, infinite impulse response (IIR) filter, moving average filter, moving median filter, wavelet transform, empirical mode decomposition, and adaptive filter in motion artefact reduction is studied and compared. The results of this study demonstrate that the adaptive filter performs better than other denoising methods, especially in dealing with the abnormal ECG signal which is measured from a patient with heart disease. In the implementation of adaptive motion artefact reduction, the results show that the use of the impedance pneumography signal as the reference input signal for the adaptive filter can effectively reduce the motion artefact in the ECG signal.

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“…There are many methods to remove BW, such as Band-pass filter [3], interpolation [4], etc. Among these methods, wavelet transform (WT) have the best result given its nature of addressing both temporal and frequency changes of a signal [5]. Nevertheless, there is a lack of research that systematically evaluates ECG baseline wander removal using different wavelet transforms.…”

Section: Introductionmentioning

confidence: 99%

Comparing Different Wavelet Transforms on Removing Electrocardiogram Baseline Wanders and Special Trends

Tsui

Chen

2020

Preprint

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Background Electrocardiogram (ECG) signal, an important indicator for heart problems, is commonly corrupted by a low-frequency baseline wander (BW) artifact, which may cause interpretation difficulty or inaccurate analysis. Unlike current state-of-the-art approach using band-pass filters, wavelet transforms can accurately capture both time and frequency information of a signal. However, extant literature is limited in applying wavelet transforms (WTs) for baseline wander removal. In this study, we aimed to evaluate 5 wavelet families with a total of 14 wavelets for removing ECG baseline wanders from a semi-synthetic dataset.Methods We created a semi-synthetic ECG dataset based on a public QT Database on Physionet repository with ECG data from 105 patients. The semi-synthetic ECG dataset comprised ECG excerpts from the QT database superimposed with artificial baseline wanders. We extracted one ECG excerpt from each of 105 patients, and the ECG excerpt comprised 14 seconds of randomly selected ECG data. 12 baseline wanders were manually generated, including sinusoidal waves, spikes and step functions. We implemented and evaluated 14 commonly used wavelets up to 12 WT levels. The evaluation metric was mean-square-error (MSE) between the original ECG excerpt and the processed signal with artificial BW removed. Results Among the 14 wavelets, Daubechies-3 wavelet and Symlets-3 wavelet with 7 levels of WT had best performance, MSE=0.0044. The average MSEs for sinusoidal waves, step, and spike functions were 0.0271, 0.0304, 0.0199 respectively. For artificial baseline wanders with spikes or step functions, wavelet transforms in general had lower performance in removing the BW; however, WTs accurately located the temporal position of an impulse edge. Conclusions We found wavelet transforms in general accurately removed various baseline wanders. Daubechies-3 and Symlets-3 wavelets performed best. The study could facilitate future real-time processing of streaming ECG signals for clinical decision support systems.

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Comparison of Baseline Wander Removal Techniques considering the Preservation of ST Changes in the Ischemic ECG: A Simulation Study

Cited by 89 publications

References 44 publications

Detection and Classification of Baseline-Wander Noise in ECG Signals Using Discrete Wavelet Transform and Decision Tree Classifier

Detection and Classification of Baseline-Wander Noise in ECG Signals Using Discrete Wavelet Transform and Decision Tree Classifier

Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring

Comparing Different Wavelet Transforms on Removing Electrocardiogram Baseline Wanders and Special Trends

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