Relevant bibliographies by topics / ECG DE-NOISING

Academic literature on the topic 'ECG DE-NOISING'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "ECG DE-NOISING"

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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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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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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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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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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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Dissertations / Theses on the topic "ECG DE-NOISING"

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SNEKHA. "GENETIC ALGORITHM BASED ECG SIGNAL DE-NOISING USING EEMD AND FUZZY THRESHOLDING." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15346.

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ElectroCardioGram (ECG) signal records electrical conduction activity of heart. These are very small signals in strength with narrow bandwidth of 0.05-120 Hz. Physicians especially cardiologist use these signals for diagnosis of the heart’s condition or heart diseases. ECG signal is contaminated with various artifacts such as Power Line Interference (PLI), Patient–electrode motion artifacts, Electrode-pop or contact noise, and Baseline Wandering and ElectroMyoGraphic (EMG) noise during acquisition. Analysis of ECG signals becomes difficult to inspect the cardiac activity in the presence of such unwanted signals. So, de-noising of ECG signal is extremely important to prevent misinterpretation of patient’s cardiac activity. Various method are available for de-noising the ECG signal such as Hybrid technique, Empirical Mode Decomposition, Un-decimated Wavelet Transform, Hilbert-Hung Transform, Adaptive Filtering, FIR Filtering, Morphological Filtering, Noise Invalidation Techniques, Non- Local Means Technique and S-Transform etc. All these techniques have some limitations such as mode mixing problem, oscillation in the reconstructed signals, reduced amplitude of the ECG signal and problem of degeneracy etc. To overcome the above mentioned limitations, a new technique is proposed for denoising of ECG signal based on Genetic Algorithm and EEMD with the help of Fuzzy Thresholding. EEMD methods are used to decompose the electrocardiogram signal into true Intrinsic Mode Functions (IMFs).Then the IMFs which are ruled by noise are automatically determined using Fuzzy Thresholding and then filtered using Genetic Particle Algorithms to remove the noise. Use of Genetic Particle Filter mitigates the sample degeneracy of Particle Filter (PF).EEMD is used in this thesis instead of EMD because it solves the EMD mode mixing problem. EEMD represents a major improvement with great versatility and robustness in noisy ECG signal filtering.
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Wang, G., Simon J. Shepherd, Clive B. Beggs, N. Rao, and Y. Zhang. "The use of kurtosis de-noising for EEG analysis of patients suffering from Alzheimer's disease." 2015. http://hdl.handle.net/10454/9242.

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The use of electroencephalograms (EEGs) to diagnose and analyses Alzheimer's disease (AD) has received much attention in recent years. The sample entropy (SE) has been widely applied to the diagnosis of AD. In our study, nine EEGs from 21 scalp electrodes in 3 AD patients and 9 EEGs from 3 age-matched controls are recorded. The calculations show that the kurtoses of the AD patients' EEG are positive and much higher than that of the controls. This finding encourages us to introduce a kurtosis-based de-noising method. The 21-electrode EEG is first decomposed using independent component analysis (ICA), and second sort them using their kurtoses in ascending order. Finally, the subspace of EEG signal using back projection of only the last five components is reconstructed. SE will be calculated after the above de-noising preprocess. The classifications show that this method can significantly improve the accuracy of SE-based diagnosis. The kurtosis analysis of EEG may contribute to increasing the understanding of brain dysfunction in AD in a statistical way.
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Book chapters on the topic "ECG DE-NOISING"

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Germán-Salló, Zoltán, Márta Germán-Salló, and Horaţiu-Ştefan Grif. "Empirical Mode Decomposition in ECG Signal De-noising." In 6th International Conference on Advancements of Medicine and Health Care through Technology; 17–20 October 2018, Cluj-Napoca, Romania, 151–55. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6207-1_24.

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Lu, Dongxin, Qi Teng, and Da Chen. "The Analysis of Wavelet De-Noising on ECG." In Lecture Notes in Electrical Engineering, 3197–203. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7618-0_411.

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Saxena, Shivani, and Ritu Vijay. "Optimal Selection of Wavelet Transform for De-noising of ECG Signal on the Basis of Statistical Parameters." In Advances in Intelligent Systems and Computing, 731–39. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2475-2_67.

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Gautam, Alka, Hoon-Jae Lee, and Wan-Young Chung. "ECG Signal De-Noising with Asynchronous Averaging and Filtering Algorithm." In Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs, 199–205. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1755-1.ch014.

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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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Nayak, Seema, Manoj Nayak, and Pankaj Pathak. "A Review on FPGA-Based Digital Filters for De-Noising ECG Signal." In Sensor Network Methodologies for Smart Applications, 1–24. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-4381-8.ch001.

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This chapter gives an overview of synthesis and analysis of digital filters on FPGA for denoising ECG signal, which provides clinical information related to heart diseases. Various types of IIR and FIR filtration techniques used for noise removal are also discussed. Many developments in the medical system technology gave birth to monitoring systems based on programmable logic devices (PLDs). Although not new to the realm of programmable devices, field programmable gate arrays (FPGAs) are becoming increasingly popular for rapid prototyping of designs with the aid of software simulation and synthesis. They are reprogrammable silicon chips, configured to implement customized hardware and are highly desirable for implementation of digital filters. The extensive literature review of various types of noise in ECG signals, filtering techniques for noise removal, and FPGA implementation are presented in this chapter.
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Zhang, Shaobai, Lihong Jiao, and Ningning Zhou. "Investigation of a Method for EEG Signal De-Noising Based on the DIVA Model." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200686.

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The DIVA (Directions Into Velocities of Articulators) model is an adaptive neural network model that is used to control the movement of the analog vocal tract to generate words, syllables, or phonemes. The input signal to the DIVA model is the EEG (electroencephalogram) signal acquired from the human brain. However, due to the influence of power frequency interference and other forms of noise, the input signal can be non-stationary and can also contain a variety of multi-form waveforms in its instantaneous structure. Input of such a signal into the DIVA model affects normal speech processing. Therefore, based on the concept of sparse decomposition, this paper applies and improves an adaptive sparse decomposition model for feature extraction of the general EEG signal structure and then uses the Matching Pursuit algorithm to compute the optimal atom. The original EEG signal can then be represented by atoms in a complete atomic library. This model removes noise from the EEG signal resulting in a better signal than the wavelet transform method. Finally, applies the EEG signal de-noised by this model to DIAV model. Simulation results show that the method improves phonetic pronunciation greatly.
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Conference papers on the topic "ECG DE-NOISING"

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Sawant, Chitrangi, and Harishchandra T. Patii. "Wavelet based ECG signal de-noising." In 2014 International Conference on Networks & Soft Computing (ICNSC). IEEE, 2014. http://dx.doi.org/10.1109/cnsc.2014.6906684.

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Tang, Jingtian, Qing Zou, Yan Tang, Bin Liu, and Xiao-kai Zhang. "Hilbert-Huang Transform for ECG De-Noising." In 2007 1st International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icbbe.2007.173.

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Aiboud, Youssef, Jamal El Mhamdi, Abdelilah Jilbab, and Hamza Sbaa. "Review of ECG signal de-noising techniques." In 2015 Third World Conference on Complex Systems (WCCS). IEEE, 2015. http://dx.doi.org/10.1109/icocs.2015.7483313.

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Tang, Guodong, and Aina Qin. "ECG De-noising Based on Empirical Mode Decomposition." In 2008 9th International Conference for Young Computer Scientists (ICYCS). IEEE, 2008. http://dx.doi.org/10.1109/icycs.2008.178.

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Li Su and Guoliang Zhao. "De-Noising of ECG Signal Using Translation- Invariant Wavelet De-Noising Method with Improved Thresholding." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1615845.

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Zou, Shenshen, and Xiaohong Zhang. "Finger ECG De-noising Based on GA-wavelet Shrinkage." In 2016 International Conference on Electrical, Mechanical and Industrial Engineering. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/icemie-16.2016.1.

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Gautam, Alka, Young-Dong Lee, and Wan-Young Chung. "ECG Signal De-noising with Signal Averaging and Filtering Algorithm." In 2008 Third International Conference on Convergence and Hybrid Information Technology (ICCIT). IEEE, 2008. http://dx.doi.org/10.1109/iccit.2008.393.

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Makwana, Gaurav, and Lalita Gupta. "De-noising of Electrocardiogram (ECG) with Adaptive Filter Using MATLAB." In 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2015. http://dx.doi.org/10.1109/csnt.2015.126.

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Kuzilek, Jakub, Vaclav Kremen, and Lenka Lhotska. "Comparison of JADE and Canonical Correlation Analysis for ECG de-noising." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6944465.

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Luo, Yu, Fengjuan Zhang, Zedong Nie, and Lei Wang. "ECG signal de-noising on node based a dedicated FFT circuit." In 2012 IEEE 10th International New Circuits and Systems Conference (NEWCAS). IEEE, 2012. http://dx.doi.org/10.1109/newcas.2012.6329056.

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