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

Tian, Xiaoying; Li, Yongshuai; Zhou, Huan; Li, Xiang; Chen, Lisha; Zhang, Xuming

doi:10.3390/s16101584

Cited by 21 publications

(14 citation statements)

References 27 publications

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“…The NLM algorithm was introduced to address the preservation of repeated structures in digital images 25 . Later, NLM was used to remove noise from ECG data 26 and further combined with the Empirical Mode Decomposition (EMD) 27 .…”

Section: Methodsmentioning

confidence: 99%

Optimal Multi-Stage Arrhythmia Classification Approach

Zheng

Chu

Struppa

et al. 2020

Sci Rep

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Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F 1 -Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F 1 -Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F 1 -Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources.ECGs represent the filtered electrical activity generated by the heart. An ECG from lead II presents a normal heartbeat under sinus rhythm that has a characteristic shape with three features, a P-wave presenting the atrial depolarization process, a QRS complex denoting the ventricular depolarization process, and a T-wave representing the ventricular repolarization. The normal feature sequence of the cardiac cycle is P-wave, QRS complex, and T-wave with sections between them called segments. Three such major segments are the PR, ST, and TP segments. Important periods within and between ECG waves are the PR, QT, and RR intervals.Damage to the heart muscle or nerves can change the electrical activity of the heart and induce a corresponding change in the shape of the ECGs. Thus, ECG is a major clinical diagnostic tool for various heart abnormalities. Arrhythmias are a family of conditions characterized by aberrations from the normal rate or rhythm of the heartbeats. There are several dozen classes of arrhythmia with various distinct manifestations, excessively slow or fast heartbeats such as sinus bradycardia and atrial tachycardia, irregular rhythm with missing or distorte...

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Section: Methodsmentioning

confidence: 99%

Optimal Multi-Stage Arrhythmia Classification Approach

Zheng

Chu

Struppa

et al. 2020

Sci Rep

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“…For the simulated ECG signal, Gaussian white noise with SNR of 0, 4, 8, 12 and 16dB, baseline wander (BW), muscle artifact (MA), and electrode motion (EM) that are with SNR of 4dB will be added to the noise-free ECG signal. It is worth noting that the clean ECG signal and four kinds of noises are simulated using Open-Source Electrophysiological Toolbox (OSET) [23]. Figure 4 shows the clean ECG signal (Figure 4(a)) and noisy one with 4 dB SNR (Figure 4(b)) that is corrupted by the aforementioned noise.…”

Section: A Evaluation On Simulated Ecg Signalmentioning

confidence: 99%

“…However, the mapping model is very sensitive to minor disturbances in either signal or noise. The last one is the mode decomposition based method, for instance, empirical mode decomposition (EMD) [18], [19], ensemble empirical mode decomposition (EEMD) [20], [21], complete ensemble empirical mode decomposition (CEEMD) [22], extreme-point symmetric mode decomposition (ESMD) [23] and variational mode decomposition (VMD) [24], [25]. The major disadvantage of these methods is mode-mixing.…”

Section: Introductionmentioning

confidence: 99%

Noise Reduction in ECG Signal Using an Effective Hybrid Scheme

et al. 2020

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Electrocardiogram (ECG) is a critical biological signal, which usually carries a great deal of essential information about patients. The high quality ECG signals are always required for a proper diagnosis of cardiac disorders. However, the raw ECG signals are highly noisy in nature. In the paper, we propose a hybrid denoising scheme to enhance ECG signals by combining high-order synchrosqueezing transform (FSSTH) with non-local means (NLM). With this method, a noisy ECG signal is first decomposed into an ensemble of intrinsic mode functions (IMFs) by FSSTH. Then, some noise is removed by eliminating a set of noisy IMFs that are determined by a scaling exponent obtained by the detrended fluctuation analysis (DFA); while the remaining IMFs are filtered by NLM. Finally, the denoised ECG signal is obtained by reconstructing the processed IMFs. Experiments are carried out using the simulated ECG signals and real ones from the MIT-BIH database, and the denoising performances are evaluated in terms of signal to noise ratio (SNR), root mean squared error (RMSE) and percent root mean square difference (PRD). Results show that the hybrid denoising scheme involving both FSSTH and NLM is able to suppress complex noise from ECG signals more effectively while preserving the details well. INDEX TERMS Electrocardiogram signal, noise reduction, high-order synchrosqueezing transform, detrended fluctuation analysis, non-local means.

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“…Recently, an ocean of noise reduction methods based on signal decomposition algorithms and Shannon entropy have been developed and used in different fields, such as acoustic signal [ 23 , 24 ], hydropower unit vibration signal [ 25 ], bearing vibration signal [ 26 ], medical signal [ 27 ], wind speed prediction [ 28 ], and so on. Xiao et al [ 29 ] proposed a fault denoising and feature extraction method of rolling bearing based on NMD and continuous wavelet transform (CWT).…”

Section: Introductionmentioning

confidence: 99%

Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

Yang

2018

Entropy

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Noise reduction of underwater acoustic signals is of great significance in the fields of military and ocean exploration. Based on the adaptive decomposition characteristic of uniform phase empirical mode decomposition (UPEMD), a noise reduction method for underwater acoustic signals is proposed, which combines amplitude-aware permutation entropy (AAPE) and Pearson correlation coefficient (PCC). UPEMD is a recently proposed improved empirical mode decomposition (EMD) algorithm that alleviates the mode splitting and residual noise effects of EMD. AAPE is a tool to quantify the information content of nonlinear time series. Unlike permutation entropy (PE), AAPE can reflect the amplitude information on time series. Firstly, the original signal is decomposed into a series of intrinsic mode functions (IMFs) by UPEMD. The AAPE of each IMF is calculated. The modes are separated into high-frequency IMFs and low-frequency IMFs, and all low-frequency IMFs are determined as useful IMFs (UIMFs). Then, the PCC between the high-frequency IMF with the smallest AAPE and the original signal is calculated. If PCC is greater than the threshold, the IMF is also determined as a UIMF. Finally, all UIMFs are reconstructed and the denoised signal is obtained. Chaotic signals with different signal-to-noise ratios (SNRs) are used for denoising experiments. Compared with EMD and extreme-point symmetric mode decomposition (ESMD), the proposed method has higher SNR and smaller root mean square error (RMSE). The proposed method is applied to noise reduction of real underwater acoustic signals. The results show that the method can further eliminate noise and the chaotic attractors are smoother and clearer.

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

Cited by 21 publications

References 27 publications

Optimal Multi-Stage Arrhythmia Classification Approach

Optimal Multi-Stage Arrhythmia Classification Approach

Noise Reduction in ECG Signal Using an Effective Hybrid Scheme

Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

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