Nonlocal Means Denoising of ECG Signals

Tracey, Brian H.; Miller, Eric L.

doi:10.1109/tbme.2012.2208964

Cited by 198 publications

(124 citation statements)

References 9 publications

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“…To evaluate the performance of the proposed ESMD-based NLM (ESMD-NLM) method, we have chosen four other denoising methods for comparison including the NLM method [24], the VMD method [29], the EEMD method [17] and the median (MED) filter [6]. Experiments have been conducted on the simulated ECG signals corrupted with Gaussian white noise and muscle artifact (MA) noise as well as the real ECG signals downloaded from MIT-BIH database, QT database and MIH-BIH ECG compression test database [29].…”

Section: Resultsmentioning

confidence: 99%

“…Based on the assumption that patterns of an image are often self-similar and redundant, the NLM method replaces a pixel considered noisy with the weighted average of all the pixels in the whole image or a pre-defined search window, where the weights are robustly determined by the nonlocal comparison of image patches instead of individual pixels. Because the NLM can achieve adequate noise suppression and excellent detail preservation results, Tracey and Miller have recently applied it to ECG signal denoising [24]. However, the NLM method cannot suppress noise in highly corrupted ECG signals well because it involves inaccurate weight computation based on noisy signals, which will lead to degraded restoration performance.…”

Section: Introductionmentioning

confidence: 99%

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Electrocardiogram Signal Denoising Using Extreme-Point Symmetric Mode Decomposition and Nonlocal Means

Tian

Zhou

et al. 2016

Sensors

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Electrocardiogram (ECG) signals contain a great deal of essential information which can be utilized by physicians for the diagnosis of heart diseases. Unfortunately, ECG signals are inevitably corrupted by noise which will severely affect the accuracy of cardiovascular disease diagnosis. Existing ECG signal denoising methods based on wavelet shrinkage, empirical mode decomposition and nonlocal means (NLM) cannot provide sufficient noise reduction or well-detailed preservation, especially with high noise corruption. To address this problem, we have proposed a hybrid ECG signal denoising scheme by combining extreme-point symmetric mode decomposition (ESMD) with NLM. In the proposed method, the noisy ECG signals will first be decomposed into several intrinsic mode functions (IMFs) and adaptive global mean using ESMD. Then, the first several IMFs will be filtered by the NLM method according to the frequency of IMFs while the QRS complex detected from these IMFs as the dominant feature of the ECG signal and the remaining IMFs will be left unprocessed. The denoised IMFs and unprocessed IMFs are combined to produce the final denoised ECG signals. Experiments on both simulated ECG signals and real ECG signals from the MIT-BIH database demonstrate that the proposed method can suppress noise in ECG signals effectively while preserving the details very well, and it outperforms several state-of-the-art ECG signal denoising methods in terms of signal-to-noise ratio (SNR), root mean squared error (RMSE), percent root mean square difference (PRD) and mean opinion score (MOS) error index.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Signal Denoising Using Extreme-Point Symmetric Mode Decomposition and Nonlocal Means

Tian

Zhou

et al. 2016

Sensors

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“…The weight, ω(i, j), takes a large value if the neighbourhood of sample i is similar to the neighbourhood of sample j; otherwise it takes a small value. It is computed as [31,32] v…”

Section: Nlm Algorithmmentioning

confidence: 99%

Rolling element bearing fault diagnosis based on non‐local means de‐noising and empirical mode decomposition

Van

Kang

Shin

2014

IET Science, Measurement & Technology

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The presence of faults in the bearings of rotating machinery is usually observed with impulses in the vibration signals. However, the vibration signals are generally non-stationary and usually contaminated by noise because of the compounded background noise present in the measuring device and the effect of interference from other machine elements. Therefore in order to enhance monitoring condition, the vibration signal needs to be properly de-noised before analysis. In this study, a novel fault diagnosis method for rolling element bearings is proposed based on a hybrid technique of non-local means (NLM) de-noising and empirical mode decomposition (EMD). An NLM which removes the noise with minimal signal distortion is first employed to eliminate or at least reduce the background noise present in the measuring device. This de-noised signal is then decomposed into a finite number of stationary intrinsic mode functions (IMF) to extract the impulsive fault features from the effect of interferences from other machine elements. Finally, envelope analyses are performed for IMFs to allow for easier detection of such characteristic fault frequencies. The results of simulated and real bearing vibration signal analyses show that the hybrid feature extraction technique of NLM de-noising, EMD and envelope analyses successfully extract impulsive features from noise signals.

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“…The ECG signal could be affected by several types of noise, which influence negatively on this signal [10][11][12]. e.g., the baseline wandering, the high frequency noise and the power line interference.…”

Section: Introductionmentioning

confidence: 99%

QRS Detection Based on an Advanced Multilevel Algorithm

Jenkal¹,

Latif²,

Toumanari³

et al. 2016

ijacsa

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Abstract-This paper presents an advanced multilevel algorithm used for the QRS complex detection. This method is based on three levels. The first permits the extraction of higher peaks using an adaptive thresholding technique. The second allows the QRS region detection. The last level permits the detection of Q, R and S waves. The proposed algorithm shows interesting results compared to recently published methods. The perspective of this work is the implementation of this method on an embedded system for a real time ECG monitoring system.

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Nonlocal Means Denoising of ECG Signals

Cited by 198 publications

References 9 publications

Electrocardiogram Signal Denoising Using Extreme-Point Symmetric Mode Decomposition and Nonlocal Means

Electrocardiogram Signal Denoising Using Extreme-Point Symmetric Mode Decomposition and Nonlocal Means

Rolling element bearing fault diagnosis based on non‐local means de‐noising and empirical mode decomposition

QRS Detection Based on an Advanced Multilevel Algorithm

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