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Journal articles on the topic 'ECG DE-NOISING'

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Author: Grafiati

Published: 11 September 2023

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1

Zhang, Sheng, Jie Gao, Jie Yang, and Shun Yu. "A Mallat Based Wavelet ECG De-Noising Algorithm." Applied Mechanics and Materials 263-266 (December 2012): 2267–70. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2267.

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A Mallat based wavelet de-noising algorithm in ECG analysis is studied. We use bior3.7 wavelet based on Mallat algorithm for ECG decomposition. Then we choose composite threshold and wavelet reconfiguration algorithm for signal de-noising to achieve an effective result. Data get from MIT/BIH is examined using the method. The result shows that it can not only remove the power frequency disturbance, EMG interference and baseline drift emerging in ECG, but also preserve the ECG characteristics.

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2

Lakshmi, P. Sri, and V. Lokesh Raju. "ECG De-noising using Hybrid Linearization Method." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 3 (September 1, 2015): 504. http://dx.doi.org/10.11591/tijee.v15i3.1568.

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<p>Electrocardiogram (ECG) is a non-invasive tool that monitors the electrical activity of the heart. An ECG signal is highly prone to the disturbances such as noise contamination, artifacts and other signals interference. So, an ECG signal has to be de-noised so that the distortions can be eliminated from the original signal for the perfect diagnosing of the condition and performance of the heart. Extended Kalman Filter (EKF) de-noises an ECG signal to some extent. This project proposes a method called Hybrid Linearization Method which is a combination of Extended Kalman Filter along with Discrete Wavelet Transform (DWT) resulting in an improved de-noised signal.</p>

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Krishna, Dr Battula Tirumala, and Putti Siva Kameswaari. "ECG Denoising Methodology using Intrinsic Time Scale Decomposition and Adaptive Switching Mean Filter." Indian Journal of Signal Processing 1, no. 2 (May 10, 2021): 7–12. http://dx.doi.org/10.35940/ijsp.b1005.051221.

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Electrocardiogram (ECG) is a widely employed tool for the analysis of cardiac disorders. A clean ECG is often desired for proper treatment of cardiac ailments. However, in the real scenario, ECG signals are corrupted with various noises during acquisition and transmission. In this article, an efficient ECG de-noising methodology using combined intrinsic time scale decomposition (ITD) and adaptive switching mean filter (ASMF) is proposed. The standard performance metric namely output SNR improvement measure the efficacy of the proposed technique at various signal to noise ratio (SNR). The proposed de-noising methodology is compared with other existing ECG de-noising approaches. A detail qualitative and quantitative study and analysis indicate that the proposed technique can be used as an effective tool for de-noising of ECG signals and hence can serve for better diagnostic in computer-based automated medical system. The performance of the proposed work is compared with existing ECG de-noising techniques namely wavelet soft thresholding based filter (DWT) [16], EMD with DWT technique [18], DWT with ADTF technique [19]. The effectiveness of the presented work has been evaluated in both qualitative and quantitative analysis. All the simulations are carried out using MATLAB software environment.

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Krishna, Dr Battula Tirumala, and Putti Siva Kameswaari. "ECG Denoising Methodology using Intrinsic Time Scale Decomposition and Adaptive Switching Mean Filter." Indian Journal of Signal Processing 1, no. 2 (May 10, 2021): 7–12. http://dx.doi.org/10.54105/ijsp.b1005.051221.

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Electrocardiogram (ECG) is a widely employed tool for the analysis of cardiac disorders. A clean ECG is often desired for proper treatment of cardiac ailments. However, in the real scenario, ECG signals are corrupted with various noises during acquisition and transmission. In this article, an efficient ECG de-noising methodology using combined intrinsic time scale decomposition (ITD) and adaptive switching mean filter (ASMF) is proposed. The standard performance metric namely output SNR improvement measure the efficacy of the proposed technique at various signal to noise ratio (SNR). The proposed de-noising methodology is compared with other existing ECG de-noising approaches. A detail qualitative and quantitative study and analysis indicate that the proposed technique can be used as an effective tool for de-noising of ECG signals and hence can serve for better diagnostic in computer-based automated medical system. The performance of the proposed work is compared with existing ECG de-noising techniques namely wavelet soft thresholding based filter (DWT) [16], EMD with DWT technique [18], DWT with ADTF technique [19]. The effectiveness of the presented work has been evaluated in both qualitative and quantitative analysis. All the simulations are carried out using MATLAB software environment.

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5

Shi, Lei, Yu Juan Si, Liu Qi Lang, Cheng Yao, and Li Li Liu. "A De-Noising Algorithm for ECG Signals Based on FIR Filter and Wavelet Transform." Advanced Materials Research 271-273 (July 2011): 247–52. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.247.

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This paper adopts a synthesis algorithm which combines FIR filters and wavelet threshold de-noising method to complete ECG de-noising. Firstly, we designed a FIR equiripple bandpass filter using Matlab FDATool to remove baseline drift, power interference and the high frequency part of muscle moments. Then we adopted an improved wavelet threshold de-noising algorithm to remove the remaining muscle moments with less decomposing levels. The algorithm was implemented on Matlab platform. The experimental results show that the algorithm is simple in design and has less calculation and good de-noising effect, which is superior to conventional wavelet threshold de-noising algorithm, and can be used in clinical analysis.

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6

Ahmed, Asia Sh, Khalida Sh Rijab, and Salwa A. Alagha. "A Study of Chosen an Optimum Type of Wavelet Filter for De-Noising an ECG signal." International Journal of Current Engineering and Technology 10, no. 05 (October 1, 2020): 749–56. http://dx.doi.org/10.14741/ijcet/v.10.5.9.

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Among various biological signals for the diagnosing of cardiac arrhythmia, Electrocardiographic (ECG) signal is the most significant one. The interesting challenge is an accurate analysis of the noisy ECG signal. Prior to accurate analysis, these signals need for de-noising to remove these unwanted noises in the signal to get an accurate diagnosis. In order to get the best de-noising results, it should have an accurate decision about the filters that we deal with for de-noising the signals. So, in this paper we present a study for choosing the optimum wavelet filter for de-noising the electrocardiograph (ECG) signal. Signals were stored as a one-dimensional matrix and series of procedure were performed to reduce the noise. The wavelets filters were chosen that very close to the original signal after applied a random-noises to the ECG signals to get familiar with the possible noise that can the signal affected with it. Also, estimation the most standard wavelet families namely Symlets, Coiflet, and Daubechies with different methods of threshold and decomposition levels were done. The purposes of this study to conclude the convenient wavelet functions in decomposition, the de-noising and the reconstruction, the method of the threshold, and the optimal decomposition level of the wavelet.

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Zhang, Dengyong, Shanshan Wang, Feng Li, Jin Wang, Arun Kumar Sangaiah, Victor S. Sheng, and Xiangling Ding. "An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter." Applied Sciences 9, no. 22 (November 18, 2019): 4968. http://dx.doi.org/10.3390/app9224968.

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Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods.

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H.D., Praveena,, Sudha, K., Geetha, P., and Venkatanaresh, M. "Comprehensive Time-Frequency Analysis of Noisy ECG Signals – A Review." CARDIOMETRY, no. 24 (November 30, 2022): 271–76. http://dx.doi.org/10.18137/cardiometry.2022.24.271275.

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This article is based on a comparison of various time-frequency analysis techniques for reducing noise in an ECG signal. Noise continuously degrades the quality of the ECG signal. Due to the ECG signal’s time-varying nature, ECG noise reduction is extremely challenging. The diagnosis of heart disorders requires an ECG signal of high quality. This study presents a survey of several techniques and noise types that can distort the ECG signal. The signal is denoised using effective denoising techniques such as the Wavelet Transform, Empirical Mode Decomposition (EMD), Empirical Wavelet Transform (EWT), Short Time Fourier Transform (STFT), Ensemble and Empirical Mode Decomposition (EEMD). Compared to previous de-noising approaches, the EWT de-noising methodology is more effective and has a lower computing complexity.

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Huang, Jian-Jia, Chung-Yu Chang, Jen-Kuang Lee, and Hen-Wai Tsao. "RESOLVING SINGLE-LEAD ECG FROM EMG INTERFERENCE IN HOLTER RECORDING BASED ON EEMD." Biomedical Engineering: Applications, Basis and Communications 26, no. 01 (February 2014): 1450008. http://dx.doi.org/10.4015/s1016237214500082.

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The aim of this study was to propose an electrocardiogram (ECG) de-noising framework based on ensemble empirical mode decomposition (EEMD) to eliminate electromyography (EMG) interference without signal distortion. ECG signals are easily corrupted by EMG, especially in Holter monitor recordings. The frequency component overlapping between EMG and ECG is a challenge in signal processing that remains to be solved. The aim of the present study, therefore, was to resolve ECG signals from recorded segments with EMG noise. Two units were put into our proposed framework; first, modified moving average filter for signal preprocessing to cancel baseline wandering, and second, EEMD to cancel EMG. In order to enhance the de-noising capability (such as signal distortion in traditional EEMD), we developed a novel EEMD signal reconstruction algorithm using a statistical ECG model. We tested the proposed framework using MIT-BIH database, artificial and single-lead recorded real-world noisy signals. Correlation coefficients and ECG morphological features were used to evaluate the performance of the proposed algorithm. Our results showed that the proposed de-noising algorithm successfully resolved ECG signals from baseline wandering and EMG interference without distorting the signal waveform.

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10

Xiong, Hui, Chunhou Zheng, Jinzhen Liu, and Limei Song. "ECG Signal In-Band Noise De-Noising Base on EMD." Journal of Circuits, Systems and Computers 28, no. 01 (October 15, 2018): 1950017. http://dx.doi.org/10.1142/s0218126619500178.

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The electrocardiogram (ECG) signal is widely used for diagnosis of heart disorders. However, ECG signal is a kind of weak signal to be interfered with heavy background interferences. Moreover, there are some overlaps between the interference frequency sub-bands and the ECG frequency sub-bands, so it is difficult to inhibit noise in the ECG signal. In this paper, the ECG signal in-band noise de-noising method based on empirical mode decomposition (EMD) is proposed. This method uses random permutation to process intrinsic mode functions (IMFs). It abstracts QRS complexes to separate them from noise so that the clean ECG signal is obtained. The method is validated through simulations on the MIT-BIH Arrhythmia Database and experiments on the measured test data. The results indicate that the proposed method can restrain noise, improve signal noise ratio (SNR) and reduce mean squared error (MSE) effectively.

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11

LAXMI, VANDANA, SWATHI J., and SASTRY D.V.L.N. "DE-NOISING OF ECG SIGNAL USING HYBRID ADAPTIVE FILTERS." i-manager's Journal on Digital Signal Processing 5, no. 1 (2017): 1. http://dx.doi.org/10.26634/jdp.5.1.13525.

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12

V, Yogesh. "Investigation of Effective De Noising Techniques for ECG Signal." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 4 (2015): 2261–68. http://dx.doi.org/10.17762/ijritcc2321-8169.1504108.

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13

Gautam, Alka, Hoon-Jae Lee, and Wan-Young Chung. "ECG Signal De-noising with Asynchronous Averaging and Filtering Algorithm." International Journal of Healthcare Information Systems and Informatics 5, no. 2 (April 2010): 30–36. http://dx.doi.org/10.4018/jhisi.2010040104.

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In this study, a new algorithm is proposed—Asynchronous Averaging and Filtering (AAF) for ECG signal de-noising. R-peaks are detected with another proposed algorithm—Minimum Slot and Maximum Point selecting method (MSMP). AAF algorithm reduces random noise (major component of EMG noise) from ECG signal and provides comparatively good results for baseline wander noise cancellation. Signal to noise ratio (SNR) improves in filtered ECG signal, while signal shape remains undistorted. The authors conclude that R-peak detection with MSMP method gives comparable results from existing algorithm like Pan-Tomkins algorithm. AAF algorithm is advantageous over adaptation algorithms like Wiener and LMS algorithm. Overall performance of proposed algorithms is comparatively good.

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Pahadiya, Pallavi, Shivani Saxena, and Ritu Vijay. "Optimisation of thresholding techniques in de-noising of ECG signals." International Journal of Intelligent Engineering Informatics 9, no. 5 (2021): 487. http://dx.doi.org/10.1504/ijiei.2021.10044780.

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Saxena, Shivani, Ritu Vijay, and Pallavi Pahadiya. "Optimisation of thresholding techniques in de-noising of ECG signals." International Journal of Intelligent Engineering Informatics 9, no. 5 (2021): 487. http://dx.doi.org/10.1504/ijiei.2021.120695.

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16

Thakran, Snekha. "A hybrid GPFA-EEMD_Fuzzy threshold method for ECG signal de-noising." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6773–82. http://dx.doi.org/10.3233/jifs-191518.

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The Electrocardiogram (ECG) signal records the electrical activity of the heart. It is very difficult for physicians to analyze the ECG signal if noise is embedded during acquisition to inspect the heart’s condition. The denoising of electrocardiogram signals based on the genetic particle filter algorithm(GPFA) using fuzzy thresholding and ensemble empirical mode decomposition (EEMD) is proposed in this paper, which efficiently removes noise from the ECG signal. This paper proposes a two-phase scheme for eliminating noise from the ECG signal. In the first phase, the noisy signal is decomposed into a true intrinsic mode function (IMFs) with the help of EEMD. EEMD is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise is obtained by using spectral flatness of each IMF and fuzzy thresholding. The corrupted IMFs are filtered using a GPF method to remove the noise. Then, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for a different local hospital database, and it gives better root mean square error and signal to noise ratio than other existing techniques (Wavelet transform (WT), EMD, Particle filter(PF) based method, extreme-point symmetric mode decomposition with Nonlocal Means(ESMD-NLM), and discrete wavelet with Savitzky-Golay(DW-SG) filter).

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M.Hamad alhussainy, Aqeel, and Ammar D. Jasim. "ECG signal de-noising based on deep learning auto encoder and discrete wavelet transform." International Journal of Engineering & Technology 9, no. 2 (April 18, 2020): 415. http://dx.doi.org/10.14419/ijet.v9i2.30499.

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ECG is very important tool for diagnosis of heart disease, this signal is suffered from different types of noises such as baseline wander (BW), muscle artifact (MA) and electrode motion (EM) , which lead to wrong interpretation. In order to prevent or reduce the effect of these noises, different approaches have been applied to enhance the ECG signal. In this paper, we have proposed a new method for ECG signal de-noising based on deep learning Auto encoder (DL-DAE) and wavelet transform named as (WT-DAE). The proposed system (WT-DAE) is constructed from two stages, in the first stage, the wavelet transform is used to isolate the most significant coefficient of the signal (approximation sub-band) from de-tails coefficients (details sub-band). The details coefficients is fed to new proposed threshold method , which is used to evaluate the threshold value according to the feature of ECG signal, this threshold value is used to threshold the detail coefficients, in order to remove the details noise that is contained as high frequencly component , then invers wavelet transform is used to reconstruct the signal . Different wavelet filters and threshold functions are applied in this stage. The second stage of signal de-noising is performed by using DAE method, which is designed for reconstruct the de-noised sig-nal. The proposed DAE model is constructed from 14 layers of convolutional, relu and max_ pooling layer with different parameters. We perform training and testing the model with MIT-BIH ECG database and the performance of the pro-posed system is evaluated by terms of MSE, RMSE, PRD and PSNR. The experimental results are compared with other approaches and show that, the proposed system demonstrated the superiority for de-noising ECG signal.

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Jaenul, Ariep, Shahad Alyousif, Ali Amer Ahmed Alrawi, and Samer K. Salih. "Robust Approach of De-noising ECG Signal Using Multi-Resolution Wavelet Transform." International Journal of Engineering & Technology 7, no. 4.11 (October 2, 2018): 5. http://dx.doi.org/10.14419/ijet.v7i4.11.20678.

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The ECG signal expresses the behavior of human heart against time. The analysis of this signal performs great information for diagnosing different cardiac diseases. In other hand, the ECG signal used for analyzing must be clean from any type of noises that corrupted it by the external environment. In this paper, a new approach of ECG signal noise reduction is proposed to minimize noise from all parts of ECG signal and maintains main characteristics of ECG signal with lowest changes. The new approach applies simple scaling down operation on the detail resolution in the wavelet transform space of noisy signal. The proposed noise reduction approach is validated by some ECG records from MIT-BIH database. Also, the performance of the proposed approach is evaluated graphically using different SNR levels and some standard metrics. The results improve the ability of the proposed approach to reduce noise from the ECG signal with high accuracy in comparison to the existing methods of noise reduction.

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Xiao, Fang Yu, Wei Tang, and Na Fu. "Wavelet Based De-Noising Using Self-Optimizing Method for ECG Signal." Applied Mechanics and Materials 416-417 (September 2013): 1214–19. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.1214.

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This paper presents a new ECG demising algorithm based on the self-optimizing method. This paper discusses the optimum threshold concept and the optimum threshold is decided by signal and threshold function. Based on the concept, this paper provides the ECG optimal method concrete steps. Through the dead value at high frequency phenomenon that is observed by experiment can identify the terminal value. Experiments show that the proposed method improves the signal-to-noise ratios. Moreover, the de-noising signals have a smooth and visual appearance.

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20

ZHANG, ZHONG, HIROSHI TODA, HISANAGA FUJIWARA, and FUJI REN. "TRANSLATION INVARIANT RI-SPLINE WAVELET AND ITS APPLICATION ON DE-NOISING." International Journal of Information Technology & Decision Making 05, no. 02 (June 2006): 353–78. http://dx.doi.org/10.1142/s0219622006001976.

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Wavelet Shrinkage using DWT has been widely used in de-noising although DWT has a translation variance problem. In this study, we solve this problem by using the translation invariant DWT. For this purpose, we propose a new complex wavelet, the Real-Imaginary Spline Wavelet (RI-Spline wavelet). We also propose the Coherent Dual-Tree algorithm for the RI-Spline wavelet and extend it to the 2-Dimensional. Then we apply this translation invariant RI-Spline wavelet for translation invariant de-noising. Experimental results show that our method, when applied to ECG data, the medical image and the textile surface inspection can obtain better de-noising results than that of conventional Wavelet Shrinkage.

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Kaviri, Vahid Makhdoomi, Masoud Sabaghi, and Saeid Marjani. "De-Noising of ECG Signals by Design of an Optimized Wavelet." Circuits and Systems 07, no. 11 (2016): 3746–55. http://dx.doi.org/10.4236/cs.2016.711314.

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Hassan, Raaed Faleh, and Sally Abdulmunem Shaker. "ECG Signal De-Noising and Feature Extraction using Discrete Wavelet Transform." International Journal of Engineering Trends and Technology 63, no. 1 (September 25, 2018): 32–39. http://dx.doi.org/10.14445/22315381/ijett-v63p206.

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Joshi, Vivek, A. R. Verma, and Y. Singh. "De-noising of ECG Signal Using Adaptive Filter Based on MPSO." Procedia Computer Science 57 (2015): 395–402. http://dx.doi.org/10.1016/j.procs.2015.07.354.

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T, Vijayakumar, Vinothkanna R, and Duraipandian M. "Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach." March 2021 3, no. 1 (March 11, 2021): 1–16. http://dx.doi.org/10.36548/jaicn.2021.1.001.

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Our human heart is classified into four sections called the left side and right side of the atrium and ventricle accordingly. Monitoring and taking care of the heart of every human is the very essential part. Therefore, the early prediction is essential to save and give awareness to humans about diet plan, lifestyle schedule. Also, this is used to improve the clinical diagnosis and treatment of any patients. To predict or identifying any cardiovascular problems, Electro Cardio Gram (ECG) is used to record the electrical signal of the heart from the body surface of humans. The algorithm learns the dataset from before cluster is called supervised; The algorithm learns to train the data from the set of a dataset is called unsupervised. Then the classification of more amount of heartbeat for different category of normal, abnormal, irregular heartbeats to detect cardiovascular diseases. In this research article, a comparison of various methods to classify the dataset with a fusion-based feature extraction method. Besides, our research work consists of a de-noising filter to reconstruct the raw data from the original input. Our proposed framework performing preprocessing that consists of a filtering approach to remove noises from the raw data set. The signal is affected by thermal noise and instrumentation noise, calibration noise due to power line fluctuation. This interference is high in many handheld devices which can be eliminated by de-noising filters. The output of the de-noising filter is input for fusion-based feature extraction and prediction model construction. This workflow progress has given good results of classifier effectiveness and imbalance arrangement conditions. We achieved good accuracy 96.5% and minimum computation time for classification of ECG signal.

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Kumar, M. Suresh, and S. Nirmala Devi. "Sparse Code Shrinkage Based ECG De-Noising in Empirical Mode Decomposition Domain." Journal of Medical Imaging and Health Informatics 5, no. 5 (September 1, 2015): 1053–58. http://dx.doi.org/10.1166/jmihi.2015.1489.

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Jain, Shweta, Varun Bajaj, and Anil Kumar. "Effective de-noising of ECG by optimised adaptive thresholding on noisy modes." IET Science, Measurement & Technology 12, no. 5 (August 1, 2018): 640–44. http://dx.doi.org/10.1049/iet-smt.2017.0203.

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Kuzilek, Jakub, Vaclav Kremen, Filip Soucek, and Lenka Lhotska. "Independent Component Analysis and Decision Trees for ECG Holter Recording De-Noising." PLoS ONE 9, no. 6 (June 6, 2014): e98450. http://dx.doi.org/10.1371/journal.pone.0098450.

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Jagannaveen, V., K. Murali Krishna, and K. Raja Rajeswari. "Noise reduction in ECG signals for bio-telemetry." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (April 1, 2019): 1028. http://dx.doi.org/10.11591/ijece.v9i2.pp1028-1035.

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<p>In Biotelemetry, Biomedical signal such as ECG is extremely important in the diagnosis of patients in remote location and is recorded commonly with noise. Considered attention is required for analysis of ECG signal to find the patho-physiology and status of patient. In this paper, LMS and RLS algorithm are implemented on adaptive FIR filter for reducing power line interference (50Hz) and (AWGN) noise on ECG signals .The ECG signals are randomly chosen from MIT_BIH data base and de-noising using algorithms. The peaks and heart rate of the ECG signal are estimated. The measurements are taken in terms of Signal Power, Noise Power and Mean Square Error.</p>

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Naveen, V. Jagan, K. Murali Krishna, and K. Raja Rajeswari. "Noise reduction in ECG Signals for Bio-telemetryb." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (February 1, 2019): 505. http://dx.doi.org/10.11591/ijece.v9i1.pp505-511.

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<p><span lang="EN-US">In Biotelemetry, Biomedical signal such as ECG is extremely important in the diagnosis of patients in remote location and is recorded commonly with noise. Considered attention is required for analysis of ECG signal to find the patho-physiology and status of patient. In this paper, LMS and RLS algorithm are implemented on adaptive FIR filter for reducing power line interference (50Hz) and (AWGN) noise on ECG signals .The ECG signals are randomly chosen from MIT_BIH data base and de-noising using algorithms. The peaks and heart rate of the ECG signal are estimated. The measurements are taken in terms of Signal Power, Noise Power and Mean Square Error.</span></p>

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Ara, Iffat, Md Najmul Hossain, and S. M. Yahea Mahbub. "Baseline Drift Removal and De-Noising of the ECG Signal using Wavelet Transform." International Journal of Computer Applications 95, no. 16 (June 18, 2014): 15–17. http://dx.doi.org/10.5120/16678-6783.

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Shi, Guo Jie, Gang Zheng, Min Dai, and Shan Ling Mou. "Study of Key Techniques on Electrocardiogram Printouts Digitizing." Key Engineering Materials 467-469 (February 2011): 1979–84. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1979.

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Key techniques were proposed in printout electrocardiogram (ECG) digitalization, which were composed by image edge detecting, angle adjusting, and ECG waveform extracting, de-noising, scaling, and saving. The requirements of digitalizing procedure contained ECG amplitude accuracy rate, heart rate counting, and morph changes. Experiments showed that the key techniques can preserve the fatal features and parameters efficiently, precisely, and automatically. The accuracy rate of waveform amplitude reaches 95%, and heart rate of that reaches 98%. The results satisfied the clinical requirements, and can be used for network medical service.

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Xu, Yang, Mingzhang Luo, Tao Li, and Gangbing Song. "ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold." Sensors 17, no. 12 (November 28, 2017): 2754. http://dx.doi.org/10.3390/s17122754.

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Bhogeshwar, Sande Seema, M. K. Soni, and Dipali Bansal. "Study of structural complexity of optimal order digital filters for de-noising ECG signal." International Journal of Biomedical Engineering and Technology 29, no. 2 (2019): 101. http://dx.doi.org/10.1504/ijbet.2019.097301.

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Bansal, Dipali, Sande Seema Bhogeshwar, and M. K. Soni. "Study of structural complexity of optimal order digital filters for de-noising ECG signal." International Journal of Biomedical Engineering and Technology 29, no. 2 (2019): 101. http://dx.doi.org/10.1504/ijbet.2019.10018401.

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Sahoo, Santanu, Prativa Biswal, Tejaswini Das, and Sukanta Sabut. "De-noising of ECG Signal and QRS Detection Using Hilbert Transform and Adaptive Thresholding." Procedia Technology 25 (2016): 68–75. http://dx.doi.org/10.1016/j.protcy.2016.08.082.

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Barrios-Muriel, Jorge, Francisco Romero, Francisco Javier Alonso, and Kostas Gianikellis. "A simple SSA-based de-noising technique to remove ECG interference in EMG signals." Biomedical Signal Processing and Control 30 (September 2016): 117–26. http://dx.doi.org/10.1016/j.bspc.2016.06.001.

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Ahmed, Y. K., and A. R. Zubair. "Performance Evaluation of Wavelet De-Noising Schemes for Suppression of Power Line Noise in Electrocardiogram Signals." Nigerian Journal of Technological Development 18, no. 2 (August 13, 2021): 144–51. http://dx.doi.org/10.4314/njtd.v18i2.9.

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Power line noise introduces distortions to recorded electrocardiogram (ECG) signals. These distortions compromise the integrity and negatively affect the interpretation of the ECG signals. Despite the fact that the amplifiers used in biomedical signal processing have high common mode rejection ratio (CMRR), ECG recordings are still often corrupted with residual Power Line Interference (PLI) noise. Further improvement in the hardware solutions do not have significant achievements in PLI noise suppression but rather introduce other adverse effects. Software approach is necessary to refine ECG data. Evaluation of PLI noise suppression in ECG signal in the wavelet domain is presented. The performance of the Hard Threshold Shrinkage Function (HTSF), the Soft Threshold Shrinkage Function (STSF), the Hyperbola Threshold Shrinkage Function (HYTSF), the Garrote Threshold Shrinkage Function (GTSF), and the Modified Garrote Threshold Shrinkage Function (MGTSF) for the suppression of PLI noise are evaluated and compared with the aid of an algorithm. The optimum tuning constant for the Modified Garrote Threshold Shrinkage Function (MGTSF) is found to be 1.18 for PLI noise. GTSF is found to have best performance closely followed by MGTSF in term of filtering Gain. HTSF recorded the lowest Gain. Filtering against PLI noise in the wavelet domain preserves the key features of the signal such as the QRS complex.

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Alickovic, Emina, and Abdulhamit Subasi. "Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases." Circuits, Systems, and Signal Processing 34, no. 2 (August 14, 2014): 513–33. http://dx.doi.org/10.1007/s00034-014-9864-8.

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Jaware, Tushar H. "Performance Investigations of Electrocardiogram Signal Using Distributed Arithmetic Mixed Signal Filter." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 29, 2017): 64. http://dx.doi.org/10.23956/ijarcsse.v7i7.99.

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A Distributed Arithmetic mixed signal filter is proposed simple moving arithmetic operations the response can be improved for real time QRS detection. Signal preprocessing and detection algorithm involves different classification methods such as wavelet based de-noising procedure which reduces noise from ECG signal. The complete structure of proposed algorithms gives accurate detection of QRS wave with high memory efficiency and speed. Algorithm performance was evaluated against the Arrhythmia Database. The numerical results indicates that Proposed algorithm finally achieved minimum false detection rate for the standard database, is functionally reliable under the condition of poor signal quality in the measured ECG data.

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Lakhera, Nidhi, A. R Verma, Bhumika Gupta, and Surjeet Singh Patel. "A Novel Approach of ECG Signal Enhancement Using Adaptive Filter Based on Whale Optimization Algorithm." Biomedical and Pharmacology Journal 14, no. 4 (December 30, 2021): 1895–903. http://dx.doi.org/10.13005/bpj/2288.

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An Electro cardiogram is commonly used in biomedical signal processing. It is used to monitor minor electrical changes in the human body. The electrical changes originate due to the function of heart. The anomalies of heart are found by ECG. In this work the Whale optimization algorithm is used to de-noising the ECG signal. The Whale optimization algorithm is used with Adaptive filter which filter the corrupted ECG signal. The performance of the ANC will be improved by calculating the optimum weight value. The WOA technique gives the best result on the different fidelity parameter compare to PSO, MPSO and ABC. The WOA technique gives the significant improvement in accuracy. It gives a good SNR, MSE, ME result compare to PSO, MPSO and ABC. The WOA gives 80% improvement in SNR 88% in MSE and 89% in ME as compared to PSO. So, by using WOA we get a desired ECG component. The WOA reduces the noise in ECG signal and improves the quality of signal.

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Shahbakhti, Mohammad, Hamed Bagheri, Babak Shekarchi, Somayeh Mohammadi, and Mohsen Naji. "A New Strategy for ECG Baseline Wander Elimination Using Empirical Mode Decomposition." Fluctuation and Noise Letters 15, no. 02 (June 2016): 1650017. http://dx.doi.org/10.1142/s0219477516500176.

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Electrocardiogram (ECG) signals might be affected by various artifacts and noises that have biological and external sources. Baseline wander (BW) is a low-frequency artifact that may be caused by breathing, body movements and loose sensor contact. In this paper, a novel method based on empirical mode decomposition (EMD) for removal of baseline noise from ECG is presented. When compared to other EMD-based methods, the novelty of this research is to reach the optimized number of decomposed levels for ECG BW de-noising using mean power frequency (MPF), while the reduction of processing time is considered. To evaluate the performance of the proposed method, a fifth-order Butterworth high pass filtering (BHPF) with cut-off frequency at 0.5[Formula: see text]Hz and wavelet approach are applied. Three performance indices, signal-to-noise ratio (SNR), mean square error (MSE) and correlation coefficient (CC), between pure and filtered signals have been utilized for qualification of presented techniques. Results suggest that the EMD-based method outperforms the other filtering method.

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Sumalatha, M., P. V. Naganjaneyulu, and K. Satya Prasad. "Low power and low area VLSI implementation of vedic design FIR filter for ECG signal de-noising." Microprocessors and Microsystems 71 (November 2019): 102883. http://dx.doi.org/10.1016/j.micpro.2019.102883.

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An-dong, Wang, Liu Lan, and Wei Qin. "An Adaptive Morphologic Filter Applied to ECG De-noising and Extraction of R Peak at Real-time." AASRI Procedia 1 (2012): 474–79. http://dx.doi.org/10.1016/j.aasri.2012.06.074.

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Allali, Abdenour, Arres Bartil, Lahcene Ziet, and Amar Hebibi. "Revealing and evaluating the influence of filters position in cascaded filter: application on the ECG de-noising performance disparity." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (February 1, 2021): 829. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp829-838.

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In this paper, a new optimization on windowing technique based on finite impulse response (FIR) filters is proposed for revealing and evaluating the Influence of filters position in cascaded filter tested on the ECG signal de-noising. baseline wander (BLW), power line interference (PLI) and electromyography (EMG) noises are getting removed. The performance of the adopted method is evaluated on the PTB diagnostic database. Subsequently, the comparisons are based on signal to noise ratio (SNR) improvement and mean square error (MSE) minimization. Where the Rectangular, and Kaiser windows have been used for the more potent performances. The disparity average (DA) of SNR values is detected; in both Kaiser and Rectangular windows are assessed by ±0.38046dB and ±0.70278dB respectively, while the MSE values were constant. The excellent configuration or filters position (H-B-L) of the filtration system is selected according to high measurements of SNR and low MSE too, to de-noise the ECG signals. First of all, this applied approach has led to 31.30 dB SNR improvement with MSE minimization of 26. 43%. This means that there is a significant contribution to improving the field of filtration.

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Kalaivani, V., R. Lakshmi Devi, and V. Anusuyadevi. "Phonocardiographic Signal and Electrocardiographic Signal Analysis for the Detection of Cardiovascular Diseases." Biosciences, Biotechnology Research Asia 15, no. 1 (March 25, 2018): 79–89. http://dx.doi.org/10.13005/bbra/2610.

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The main objective is to develop a novel method for the heart sound analysis for the detection of cardiovascular diseases. It can be considered as one of the important phases in the automated analysis of PCG signals. Heart sounds carry information about mechanical activity of the cardiovascular system. This information includes specific physiological state of the subject and the short term variability related to the respiratory cycle. The interpretation of sounds and extraction of changes in the physiological state while maintaining the short term variability are still an open problem and is subject of this paper. The system deals with the process of de-noising of the heart sound signal(PCG) and the signal is decomposed into several sub-bands and the de-noised heart sound signal is segmented into the basic heart sounds S1 and S2, along with the systolic and diastolic interval.. Also, the ECG signal is de-noised. Meanwhile, the R-peaks are identified from the ECG signal and RR interval is obtained. Extraction of features are done from both the heart sound signal and the ECG signal. From the features, the R-peaks are identified from the ECG signal and RR interval is obtained. The attribute selection is to find the best attribute values that can be used for the classification process. Finally, using classification technique, cardiac diseases are detected. This work is implemented by using MATLAB software.

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Padmavathy, T. V., S. Saravanan, and M. N. Vimalkumar. "Partial product addition in Vedic design-ripple carry adder design fir filter architecture for electro cardiogram (ECG) signal de-noising application." Microprocessors and Microsystems 76 (July 2020): 103113. http://dx.doi.org/10.1016/j.micpro.2020.103113.

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Mohsen Alkanfery, Hadi, and Ibrahim Mustafa Mehedi. "Fractional Order Butterworth Filter for Fetal Electrocardiographic Signal Feature Extraction." Signal & Image Processing : An International Journal 12, no. 05 (October 31, 2021): 45–56. http://dx.doi.org/10.5121/sipij.2021.12503.

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The non-invasive Fetal Electrocardiogram (FECG) signal has become a significant method for monitoring the fetus's physiological conditions, extracted from the Abdominal Electrocardiogram (AECG) during pregnancy. The current techniques are limited during delivery for detecting and analyzing fECG. The non - intrusive fECG recorded from the mother's abdomen is contaminated by a variety of noise sources, can be a more challenging task for removing the maternal ECG. These contaminated noises have become a major challenge during the extraction of fetal ECG is managed by uni-modal technique. In this research, a new method based on the combination of Wavelet Transform (WT) and Fast Independent Component Analysis (FICA) algorithm approach to extract fECG from AECG recordings of the pregnant woman is proposed. Initially, preprocessing of a signal is done by applying a Fractional Order Butterworth Filter (FBWF). To select the Direct ECG signal which is characterized as a reference signal and the abdominal signal which is characterized as an input signal to the WT, the cross-correlation technique is used to find the signal with greater similarity among the available four abdominal signals. The model performance of the proposed method shows the most frequent similarity of fetal heartbeat rate present in the database can be evaluated through MAE and MAPE is 0.6 and 0.041209 respectively. Thus the proposed methodology of de-noising and separation of fECG signals will act as the predominant one and assist in understanding the nature of the delivery on further analysis.

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Wu, Yin Chao, Seong Jin Noh, and Suyun Ham. "Identification of Inundation Using Low-Resolution Images from Traffic-Monitoring Cameras: Bayes Shrink and Bayesian Segmentation." Water 12, no. 6 (June 17, 2020): 1725. http://dx.doi.org/10.3390/w12061725.

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This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. For the comparative assessment of de-noising techniques, the Bayes shrink and three conventional methods are compared. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. For the comparative assessment of image segmentation techniques, k-means segmentation, Otsu segmentation, and Bayesian segmentation are compared. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. By using the principle of the image processing presented in this paper, we can estimate the inundation from images and assess flooding risks in the vicinity of local flooding locations. Such information will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.

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Cui, Zhi, and Xian-pu Cui. "Wireless Multimedia Sensor Network Image De-Noising via a Detail-Preserving Sparse Model." Cybernetics and Information Technologies 15, no. 6 (December 1, 2015): 57–69. http://dx.doi.org/10.1515/cait-2015-0067.

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Abstract In this paper, we propose a Detail-Preserving Sparse Model (DPSM) for de-noising of images that are usually interfered by noise on the Wireless Multimedia Sensor Network (WMSN). Specifically, based on the Structural SIMilarity (SSIM), the DPSM first incorporates a structural-preserving constraint, which enables the structure in the reconstructed image to be close to the ideal no-noise image. In addition, the DPSM adopts a residual ratio as the stopping condition of the sparse solution algorithm (e.g., Orthogonal Matching Pursuit), which enables the structures to be reconstructed under high noise conditions. The experimental results on several WMSN images have demonstrated the superiority of the proposed DPSM method over several well-known de-noising approaches in terms of PSNR and SSIM.

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Yang, Zhenjie, Chao Sun, Junwei Ye, Congying Gan, Yue Li, Lingyu Wang, and Yujun Chen. "Spatio-Temporal Heterogeneity of Ecological Quality in Hangzhou Greater Bay Area (HGBA) of China and Response to Land Use and Cover Change." Remote Sensing 14, no. 21 (November 7, 2022): 5613. http://dx.doi.org/10.3390/rs14215613.

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Human activities have been stressing the ecological environment since we stepped into the Anthropocene Age. It is urgent to formulate a sustainable plan for balancing socioeconomic development and ecological conservation based on a thorough understanding of ecological environment changes. The ecological environment can be evaluated when multiple remote sensing indices are integrated, such as the use of the recently prevalent Remote Sensing-based Ecological Index (RSEI). Currently, most of the RSEI-related studies have focused on the ecological quality evolution in small areas. Less attention was paid to the spatio-temporal heterogeneity of ecological quality in large-scale urban agglomerations and the potential links with Land Use and Cover Change (LUCC). In this study, we monitored the dynamics of the ecological quality in the Hangzhou Greater Bay Area (HGBA) during 1995–2020, using the RSEI as an indicator. During the construction of the RSEI, a percentile de-noising normalization method was proposed to overcome the problem of widespread noises from large-scale regions and make the RSEI-based ecological quality assessment for multiple periods comparable. Combined with the land use data, the quantitative relationship between the ecological quality and the LUCC was revealed. The results demonstrated that: (1) The ecological quality of the HGBA degraded after first improving but was still good (averaged RSEI of 0.638). It was divergent for the prefecture-level cities of the HGBA, presenting degraded, improved, and fluctuant trends for the cities from north to south. (2) For ecological quality, the improved regions have larger area (57.5% vs. 42.5%) but less increment (0.141 vs. −0.195) than the degraded regions. Mountains, downtowns, and coastal wetlands were the hot spots for the improvement and urbanization, and reclamation processes were responsible for the degradation. (3) The ecological quality was improved for forests and urban areas (△RSEI > 0.07) but degraded for farmland (∆RSEI = −0.03). As a result, the ecological cost was reduced among human-dominant environments (e.g., farmland, urban areas) while enlarged for the conversion from nature-(e.g., forests) to human-dominant environments.

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