Review of noise removal techniques in ECG signals

Chatterjee, Shubhojeet; Thakur, R. S.; Yadav, Ram Narayan; Gupta, Lalita; Raghuvanshi, Deepak Kumar

doi:10.1049/iet-spr.2020.0104

Cited by 194 publications

(90 citation statements)

References 143 publications

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“…This method was verified using synthetic tests and MS signal denoising. The main conclusions are summarised as follows: (1) The proposed method eliminates high-frequency noise through EEMD and the variance contribution rate; subsequently, a Hankel matrix is constructed with each IMF for PCA denoising. PCA and soft thresholding can denoise low-frequency components, as well as retain signal characteristics.…”

Section: Discussionmentioning

confidence: 99%

EEMD and Multiscale PCA-Based Signal Denoising Method and Its Application to Seismic P-Phase Arrival Picking

Peng

Guo

Shang

2021

Sensors

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Signal denoising is one of the most important issues in signal processing, and various techniques have been proposed to address this issue. A combined method involving wavelet decomposition and multiscale principal component analysis (MSPCA) has been proposed and exhibits a strong signal denoising performance. This technique takes advantage of several signals that have similar noises to conduct denoising; however, noises are usually quite different between signals, and wavelet decomposition has limited adaptive decomposition abilities for complex signals. To address this issue, we propose a signal denoising method based on ensemble empirical mode decomposition (EEMD) and MSPCA. The proposed method can conduct MSPCA-based denoising for a single signal compared with the former MSPCA-based denoising methods. The main steps of the proposed denoising method are as follows: First, EEMD is used for adaptive decomposition of a signal, and the variance contribution rate is selected to remove components with high-frequency noises. Subsequently, the Hankel matrix is constructed on each component to obtain a higher order matrix, and the main score and load vectors of the PCA are adopted to denoise the Hankel matrix. Next, the PCA-denoised component is denoised using soft thresholding. Finally, the stacking of PCA- and soft thresholding-denoised components is treated as the final denoised signal. Synthetic tests demonstrate that the EEMD-MSPCA-based method can provide good signal denoising results and is superior to the low-pass filter, wavelet reconstruction, EEMD reconstruction, Hankel–SVD, EEMD-Hankel–SVD, and wavelet-MSPCA-based denoising methods. Moreover, the proposed method in combination with the AIC picking method shows good prospects for processing microseismic waves.

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Section: Discussionmentioning

confidence: 99%

EEMD and Multiscale PCA-Based Signal Denoising Method and Its Application to Seismic P-Phase Arrival Picking

Peng

Guo

Shang

2021

Sensors

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“…Several approaches to signal de-noising and decomposition have been presented including but not limited to the sparsity-based models, empirical mode decomposition (EMD) whose functionality is based on the Hilbert Huang transform (HHT), discrete wavelet transform (DWT), Bayesian-filter-based methods, etc. [19], [20]. Of these techniques, the EMD and DWT are the most popular due to their robustness for noise reduction/elimination; however, studies show that the wavelet decomposition has better de-noising capabilities and this has motivated its usage in this study [20].…”

Section: A Signal De-noisingmentioning

confidence: 99%

“…[19], [20]. Of these techniques, the EMD and DWT are the most popular due to their robustness for noise reduction/elimination; however, studies show that the wavelet decomposition has better de-noising capabilities and this has motivated its usage in this study [20].…”

Section: A Signal De-noisingmentioning

confidence: 99%

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

Akpudo

Hur

2021

IEEE Access

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The remaining useful life (RUL) prediction of industrial cyber-physical system components demands the use of reliable prognostics parameters and frameworks. Against the traditional use of a single measure of degradation, data from multiple sensors provide abundant characteristic information for modeling, assessing, and extracting useful parameters via appropriate signal processing and sensor fusion methods. This study introduces a multi-sensor prognostics approach which merges highly prognosible statistical features from vibrational and pressure sensor measurements after a multi-level wavelet decomposition of the signals. The prognostic algorithm presented in this work for solenoid pump RUL prediction is a multi-objective genetic algorithm-optimized long short-term memory (MOGA-LSTM) which accepts the fused sensor features as input and returns the RUL of the pump as output. The framework was tested on a run-to-failure experiment on a VSC63A5 Solenoid pump following a significant pump malfunction caused by a clogged suction filter after the test. Using standard prognostic performance evaluation metrics, the performance of the prognostics framework was compared with other reliable state-of-the-art methods with a remarkable comparative advantage in addition to better automation potentials for real-time condition monitoring and RUL prediction.

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“…However, the peak-topeak amplitudes of ECG artifacts in bipolar recordings in vivo might be unstable due to respiration or motion. Previous literature reported that respiration or even relatively slow motion over time would induce baseline wander in the ECG signals (Satija et al, 2018;Chatterjee et al, 2020). The peak-to-peak amplitudes of ECG spikes vary by 15% due to baseline wander (Friesen et al, 1990;Satija et al, 2018).…”

Section: Using Simultaneous Monopolar Recording As An Ecg Referencementioning

confidence: 99%

Removal of Electrocardiogram Artifacts From Local Field Potentials Recorded by Sensing-Enabled Neurostimulator

Chen

Hao

et al. 2021

Front. Neurosci.

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Sensing-enabled neurostimulators are an advanced technology for chronic observation of brain activities, and show great potential for closed-loop neuromodulation and as implantable brain-computer interfaces. However, local field potentials (LFPs) recorded by sensing-enabled neurostimulators can be contaminated by electrocardiogram (ECG) signals due to complex recording conditions and limited common-mode-rejection-ratio (CMRR). In this study, we propose a solution for removing such ECG artifacts from local field potentials (LFPs) recorded by a sensing-enabled neurostimulator. A synchronized monopolar channel was added as an ECG reference, and two pre-existing methods, i.e., template subtraction and adaptive filtering, were then applied. ECG artifacts were successfully removed and the performance of the method was insensitive to residual stimulation artifacts. This approach to removal of ECG artifacts broadens the range of applications of sensing-enabled neurostimulators.

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Review of noise removal techniques in ECG signals

Cited by 194 publications

References 143 publications

EEMD and Multiscale PCA-Based Signal Denoising Method and Its Application to Seismic P-Phase Arrival Picking

EEMD and Multiscale PCA-Based Signal Denoising Method and Its Application to Seismic P-Phase Arrival Picking

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

Removal of Electrocardiogram Artifacts From Local Field Potentials Recorded by Sensing-Enabled Neurostimulator

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