ECG signals denoising method based on improved wavelet threshold algorithm

AbstractElectrocardiogram (ECG) is an effective and non-invasive indicator for the detection and prevention of arrhythmia. ECG signals are susceptible to noise contamination, which can lead to errors in ECG interpretation. Therefore, ECG pretreatment is important for accurate analysis. In this paper, a method of noise reduction based on deep learning is proposed. The method is divided into two stages, and two corresponding models are formed. In the first stage, a one-dimensional U-net model is designed for ECG signal denoising to eliminate noise as much as possible. The one-dimensional DR-net model in the second stage is used to reconstruct the ECG signal and to correct the waveform distortion caused by noise removal in the first stage. In this paper, the U-net and the DR-net are constructed by the convolution method to achieve end-to-end mapping from noisy ECG signals to clean ECG signals. The ECG data used in this paper are from CPSC2018, and the noise signal is from MIT-BIH Noise Stress Test Database (NSTDB). In the experiment, the improvement in the signal-to-noise ratio SNRimp, the root mean square error decrease RMSEde, and the correlation coefficient P, are used to evaluate the performance of the network. This two-stage method is compared with FCN and U-net alone. The experimental results show that the two-stage noise reduction method can eliminate complex noise in the ECG signal while retaining the characteristic shape of the ECG signal. According to the results, we believe that the proposed method has a good application prospect in clinical practice.

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Application of Improved EMD Threshold Algorithm in the Study of the Electric Life State of the AC Contactor

MATEC Web of Conferences ◽

10.1051/matecconf/201823203040 ◽

2018 ◽

Vol 232 ◽

pp. 03040

Author(s):

Yiping Bo ◽

Liang Zhang ◽

Shuxin Liu ◽

Yanjun Zhang ◽

Yundong Cao

Keyword(s):

Empirical Mode Decomposition ◽

Background Noise ◽

Assessment Model ◽

Threshold Function ◽

Characteristic Parameters ◽

Threshold Method ◽

Denoising Method ◽

Threshold Algorithm ◽

Mode Decomposition ◽

Better Than

The condition monitoring signal of electrical life of AC contactors has characteristics such as non-linear, non-stationary and strong background noise. The premise and basis for the accurate establishment of the assessment model of the electrical life of AC contactors is how to accurately extract the characteristic parameters of electrical life signals. Therefore, it is of great significance for the study of AC contactors’ electrical life to preproccess its characteristic parameters of electrical life signals. Adopting traditional soft and hard methods cannot remove the noise effectively, therefore, a new threshold function can be proposed, and further model the wavelet threshold denoising theory to research the threshold denoising method of empirical mode decomposition (EMD), denoise the main parameters which can influence the state of AC contactors. The experimental results show that adopting the improved EMD threshold algorithm can effectively remove the noise, and it is better than adopting wavelet threshold method or empirical mode decomposition alone

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RANDOM INTERPOLATION AVERAGE FOR ECG SIGNAL DENOISING USING MULTIPLE WAVELET BASES

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237213500427 ◽

2013 ◽

Vol 25 (04) ◽

pp. 1350042 ◽

Cited By ~ 1

Author(s):

Ying Yang ◽

Yusen Wei

Keyword(s):

Signal To Noise Ratio ◽

Signal Denoising ◽

Ecg Signal ◽

Wavelet Bases ◽

Denoising Method ◽

Translation Invariant ◽

The Real ◽

Ecg Signals ◽

Snr Improvement ◽

Electrocardiogram Ecg

The random interpolation average (RIA) is a simple yet good denoising method. It firstly employed several times of random interpolations to a noisy signal, then applied the wavelet transform (WT) denoising to each interpolated signal and averaged all of the denoised signals to finish the denoising process. In this paper, multiple wavelet bases and the level-dependent threshold estimator were used in the RIA scheme so that it can be more suitable for the electrocardiogram (ECG) signal denoising. The synthetic ECG signal, real ECG signal and four types of noise were used to perform comparison experiments. The results show that the proposed method can provide the best signal to noise ratio (SNR) improvement in the deoising applications of the synthetic ECG signal and the real ECG signals. For the real ECG signals denoising, the average SNR improvement is 5.886 dB, while the result of the RIA scheme with single wavelet basis (RIAS), the fully translation-invariant [TI (fully)] and the WT denoising using hard thresholding [WT (hard)] are 5.577, 5.274 and 3.484 dB, respectively.

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A novel intelligent denoising method of ecg signals based on wavelet adaptive threshold and mathematical morphology

Applied Intelligence ◽

10.1007/s10489-022-03182-3 ◽

2022 ◽

Author(s):

Li Gao ◽

Yi Gan ◽

Juncheng Shi

Keyword(s):

Mathematical Morphology ◽

Adaptive Threshold ◽

Denoising Method ◽

Ecg Signals

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An efficient ECG Denoising method using Discrete Wavelet with Savitzky-Golay filter

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2019-0097 ◽

2019 ◽

Vol 5 (1) ◽

pp. 385-387 ◽

Cited By ~ 2

Author(s):

Fars Samann ◽

Thomas Schanze

Keyword(s):

Signal To Noise Ratio ◽

Gaussian White Noise ◽

Optimal Choice ◽

Discrete Wavelet ◽

Denoising Method ◽

Frame Size ◽

Polynomial Degree ◽

Ecg Signals ◽

Ecg Denoising ◽

Mean Square Difference

AbstractElectrocardiogram (ECG) is a widely used tool for the early diagnosis and evaluation of cardiac disorders. The ECG signal is usually distorted during recording by different types of noise which may lead to incorrect diagnosis. Therefore, clear ECG signals are required to support good cardiac disorder diagnosing. In this paper, an efficient ECG denoising method using combined discrete wavelet with Savitzky-Golay (S-G) filter is proposed. The performance of S-G filter is studied in terms of polynomial degree and frame size, i.e. signal section. In addition, the performance of denoising wavelet is studied in term of mother wavelet type and wavelet order. The advantage of S-G filter is combined with discrete wavelet denoising method to get better denoising performance. The performance of denoising ECG are evaluated using signal to noise ratio (SNR) and percentage root mean square difference (PRD). For this we used simulated and gaussian white noise surrogated ECG signals. Our results show that combined S-G and wavelet filter denoising is noticeable better than the respective individual procedures. In addition, we found that the selection of frame size, order of the S-G filter and the wavelet type and order should be done carefully in order to get optimal results. It also holds true for the new filter that the optimal choice of filter parameters is a compromise between noise reduction and distortion.

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An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function

Wireless Communications and Mobile Computing ◽

10.1155/2020/8811962 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Dengyong Zhang ◽

Shanshan Wang ◽

Feng Li ◽

Shang Tian ◽

Jin Wang ◽

...

Keyword(s):

Empirical Mode Decomposition ◽

Sample Entropy ◽

Threshold Function ◽

Signal Denoising ◽

Ecg Signal ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Ecg Signals ◽

Mode Decomposition ◽

Ecg Denoising

The electrocardiogram (ECG) signal can easily be affected by various types of noises while being recorded, which decreases the accuracy of subsequent diagnosis. Therefore, the efficient denoising of ECG signals has become an important research topic. In the paper, we proposed an efficient ECG denoising approach based on empirical mode decomposition (EMD), sample entropy, and improved threshold function. This method can better remove the noise of ECG signals and provide better diagnosis service for the computer-based automatic medical system. The proposed work includes three stages of analysis: (1) EMD is used to decompose the signal into finite intrinsic mode functions (IMFs), and according to the sample entropy of each order of IMF following EMD, the order of IMFs denoised is determined; (2) the new threshold function is adopted to denoise these IMFs after the order of IMFs denoised is determined; and (3) the signal is reconstructed and smoothed. The proposed method solves the shortcoming of discarding the first-order IMF directly in traditional EMD denoising and proposes a new threshold denoising function to improve the traditional soft and hard threshold functions. We further conduct simulation experiments of ECG signals from the MIT-BIH database, in which three types of noise are simulated: white Gaussian noise, electromyogram (EMG), and power line interference. The experimental results show that the proposed method is robust to a variety of noise types. Moreover, we analyze the effectiveness of the proposed method under different input SNR with reference to improving SNR ( SNR imp ) and mean square error ( MSE ), then compare the denoising algorithm proposed in this paper with previous ECG signal denoising techniques. The results demonstrate that the proposed method has a higher SNR imp and a lower MSE . Qualitative and quantitative studies demonstrate that the proposed algorithm is a good ECG signal denoising method.

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Design of Portable Electrocardiograph Monitoring System

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.3.030403 ◽

2020 ◽

Author(s):

Jia Hua-Ping ◽

Zhao Jun-Long ◽

Liu Jun

Keyword(s):

Real Time ◽

Monitoring System ◽

Data Storage ◽

P Wave ◽

Ecg Monitoring ◽

The Public ◽

Ecg Signals ◽

Early Warning Mechanism ◽

Timely Treatment ◽

Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

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