Review of noise removal techniques in ECG signals
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Ram Narayan Yadav | Lalita Gupta | Shubhojeet Chatterjee | Rini Smita Thakur | Deepak Kumar Raghuvanshi | Lalita Gupta | R. Yadav | D. Raghuvanshi | R. Thakur | S. Chatterjee
[1] Maryam Mohebbi,et al. ECG Denoising Using Marginalized Particle Extended Kalman Filter With an Automatic Particle Weighting Strategy , 2017, IEEE Journal of Biomedical and Health Informatics.
[2] Joel A. Tropp,et al. Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.
[3] Antônio Cláudio Paschoarelli Veiga,et al. Electrocardiogram signal denoising by a new noise variation estimate , 2020 .
[4] Binqiang Chen,et al. Centralized Wavelet Multiresolution for Exact Translation Invariant Processing of ECG Signals , 2019, IEEE Access.
[5] Hsin-Yi Lin,et al. Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals , 2014 .
[6] Salim Lahmiri,et al. Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. , 2014, Healthcare technology letters.
[7] Rama Komaragiri,et al. Heart rate monitoring and therapeutic devices: A wavelet transform based approach for the modeling and classification of congestive heart failure. , 2018, ISA transactions.
[8] Laura Frølich,et al. Removal of muscular artifacts in EEG signals: a comparison of linear decomposition methods , 2018, Brain Informatics.
[9] Prabin Kumar Bora,et al. Electrocardiogram signal denoising using non-local wavelet transform domain filtering , 2015, IET Signal Process..
[10] Evangelia I. Zacharaki,et al. Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients , 2015, Expert Syst. Appl..
[11] B. Lin,et al. Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview , 2017 .
[12] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[13] Zishu He,et al. Null broadening adaptive beamforming based on covariance matrix reconstruction and similarity constraint , 2017, EURASIP J. Adv. Signal Process..
[14] B. V. K. Vijaya Kumar,et al. Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals , 2012, IEEE Transactions on Biomedical Engineering.
[15] Udit Satija,et al. Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal , 2017, Healthcare technology letters.
[16] Y. Benjamini,et al. Adaptive thresholding of wavelet coefficients , 1996 .
[17] Robert Woolard,et al. Missed Diagnoses of Acute Cardiac Ischemia in the Emergency Department , 2000 .
[18] Rama Komaragiri,et al. Design of wavelet transform based electrocardiogram monitoring system. , 2018, ISA transactions.
[19] Rama Komaragiri,et al. Efficient QRS complex detection algorithm based on Fast Fourier Transform , 2018, Biomedical Engineering Letters.
[20] Yu Tsao,et al. Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders , 2019, IEEE Access.
[21] Feng Lin,et al. A stacked contractive denoising auto-encoder for ECG signal denoising , 2016, Physiological measurement.
[22] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[23] Guy P. Nason,et al. A ‘nondecimated’ lifting transform , 2009, Stat. Comput..
[24] I. Johnstone,et al. Ideal spatial adaptation by wavelet shrinkage , 1994 .
[25] Norden E. Huang,et al. On the Filtering Properties of the Empirical Mode Decomposition , 2010, Adv. Data Sci. Adapt. Anal..
[26] G.B. Moody,et al. The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.
[27] Mohammad Bagher Shamsollahi,et al. ECG Denoising and Compression Using a Modified Extended Kalman Filter Structure , 2008, IEEE Transactions on Biomedical Engineering.
[28] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[29] Jong-Myon Kim,et al. Adaptive ECG denoising using genetic algorithm-based thresholding and ensemble empirical mode decomposition , 2016, Inf. Sci..
[30] Susmita Das,et al. Hybrid approach for ECG signal enhancement using dictionary learning‐based sparse representation , 2019, IET Science, Measurement & Technology.
[31] Jeffrey M. Hausdorff,et al. Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .
[32] Allan Kardec Barros,et al. independent , 2006, Gumbo Ya Ya.
[33] Kevin Kaergaard,et al. A comprehensive performance analysis of EEMD-BLMS and DWT-NN hybrid algorithms for ECG denoising , 2016, Biomed. Signal Process. Control..
[34] N. Huang,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[35] Bernard W. Silverman,et al. The discrete wavelet transform in S , 1994 .
[36] H A Fozzard,et al. AZTEC, a preprocessing program for real-time ECG rhythm analysis. , 1968, IEEE transactions on bio-medical engineering.
[37] Brij N. Singh,et al. Optimal selection of wavelet basis function applied to ECG signal denoising , 2006, Digit. Signal Process..
[38] Celia Shahnaz,et al. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains , 2012, Biomed. Signal Process. Control..
[39] Norden E. Huang,et al. Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..
[40] Prasanna Kumar Sahu,et al. Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique , 2018 .
[41] Hyunggon Park,et al. ECG Authentication System Design Based on Signal Analysis in Mobile and Wearable Devices , 2016, IEEE Signal Processing Letters.
[42] Joel A. Tropp,et al. Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.
[43] Ming Liu,et al. ECG signal enhancement based on improved denoising auto-encoder , 2016, Eng. Appl. Artif. Intell..
[44] Wei Li,et al. Wavelets for Electrocardiogram: Overview and Taxonomy , 2019, IEEE Access.
[45] Sos S. Agaian,et al. A Wavelet-Denoising Approach Using Polynomial Threshold Operators , 2008, IEEE Signal Processing Letters.
[46] J. van Alsté,et al. Removal of Base-Line Wander and Power-Line Interference from the ECG by an Efficient FIR Filter with a Reduced Number of Taps , 1985, IEEE Transactions on Biomedical Engineering.
[47] Susmita Das,et al. An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter , 2018, Biomed. Signal Process. Control..
[48] Rachid Latif,et al. An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform , 2016 .
[49] Steffen Leonhardt,et al. Ambient and Unobtrusive Cardiorespiratory Monitoring Techniques , 2015, IEEE Reviews in Biomedical Engineering.
[50] Minglei Shu,et al. ECG Baseline Wander Correction and Denoising Based on Sparsity , 2019, IEEE Access.
[51] C. Stein. Estimation of the Mean of a Multivariate Normal Distribution , 1981 .
[53] Gayadhar Pradhan,et al. Variational mode decomposition based ECG denoising using non-local means and wavelet domain filtering , 2018, Australasian Physical & Engineering Sciences in Medicine.
[54] M. P. S. Chawla,et al. PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: A survey and comparison , 2011, Appl. Soft Comput..
[55] Eric L. Miller,et al. Nonlocal Means Denoising of ECG Signals , 2012, IEEE Transactions on Biomedical Engineering.
[56] Xiaolu Li,et al. Electrocardiograph signal denoising based on sparse decomposition , 2017, Healthcare technology letters.
[57] El-Sayed A. El-Dahshan,et al. Genetic algorithm and wavelet hybrid scheme for ECG signal denoising , 2011, Telecommun. Syst..
[58] Lionel Tarassenko,et al. Application of independent component analysis in removing artefacts from the electrocardiogram , 2006, Neural Computing & Applications.
[59] G. Pradhan,et al. Denoising of ECG signal by non-local estimation of approximation coefficients in DWT , 2017 .
[60] Anil Kumar,et al. Riemann Liouvelle Fractional Integral Based Empirical Mode Decomposition for ECG Denoising , 2018, IEEE Journal of Biomedical and Health Informatics.
[61] Ivan W. Selesnick,et al. Sparse Regularization via Convex Analysis , 2017, IEEE Transactions on Signal Processing.
[62] Guoqiang Han,et al. Electrocardiogram signal denoising based on a new improved wavelet thresholding. , 2016, The Review of scientific instruments.
[63] W. K. Lee,et al. Smart ECG Monitoring Patch with Built-in R-Peak Detection for Long-Term HRV Analysis , 2015, Annals of Biomedical Engineering.
[64] M. Awal,et al. An adaptive level dependent wavelet thresholding for ECG denoising , 2014 .
[65] Nasser Mourad,et al. ECG denoising algorithm based on group sparsity and singular spectrum analysis , 2019, Biomed. Signal Process. Control..
[66] Gabriel Peyré. Best basis compressed sensing , 2010, IEEE Trans. Signal Process..
[67] Maryam Mohebbi,et al. An Adaptive Particle Weighting Strategy for ECG Denoising Using Marginalized Particle Extended Kalman Filter: An Evaluation in Arrhythmia Contexts , 2017, IEEE Journal of Biomedical and Health Informatics.
[68] C Marque,et al. Adaptive filtering for ECG rejection from surface EMG recordings. , 2005, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.
[69] I. Johnstone,et al. Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .
[70] Christian Jutten,et al. A Nonlinear Bayesian Filtering Framework for ECG Denoising , 2007, IEEE Transactions on Biomedical Engineering.
[71] Lalita Gupta,et al. State-of-art analysis of image denoising methods using convolutional neural networks , 2019, IET Image Process..
[72] Antônio Cláudio Paschoarelli Veiga,et al. Electrocardiogram signal denoising by clustering and soft thresholding , 2018, IET Signal Process..
[73] Kang-Ming Chang,et al. Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition , 2010, Sensors.
[74] Omkar Singh,et al. ECG signal denoising via empirical wavelet transform , 2017, Australasian Physical & Engineering Sciences in Medicine.
[75] B. Vidakovic. Nonlinear wavelet shrinkage with Bayes rules and Bayes factors , 1998 .
[76] Manuel Blanco-Velasco,et al. ECG signal denoising and baseline wander correction based on the empirical mode decomposition , 2008, Comput. Biol. Medicine.
[77] E. Braunwald,et al. Survival of patients with severe congestive heart failure treated with oral milrinone. , 1986, Journal of the American College of Cardiology.
[78] H. T. Nagle,et al. A comparison of the noise sensitivity of nine QRS detection algorithms , 1990, IEEE Transactions on Biomedical Engineering.
[79] Laurent Condat,et al. A Direct Algorithm for 1-D Total Variation Denoising , 2013, IEEE Signal Processing Letters.
[80] M. Sabarimalai Manikandan,et al. A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment , 2018, IEEE Reviews in Biomedical Engineering.
[81] A. A. Armoundas,et al. ECG denoising and fiducial point extraction using an extended Kalman filtering framework with linear and nonlinear phase observations , 2016, Physiological measurement.
[82] Jian Yang,et al. Robust Adaptive Modified Newton Algorithm for Generalized Eigendecomposition and Its Application , 2007, EURASIP J. Adv. Signal Process..
[83] Khaled Daqrouq,et al. ECG Signal Denoising By Wavelet Transform Thresholding , 2008 .
[84] Rama Komaragiri,et al. Design of efficient fractional operator for ECG signal detection in implantable cardiac pacemaker systems , 2019, Int. J. Circuit Theory Appl..
[85] D. Donoho,et al. Atomic Decomposition by Basis Pursuit , 2001 .
[86] E. Parzen,et al. Data dependent wavelet thresholding in nonparametric regression with change-point applications , 1996 .
[87] Salim Lahmiri,et al. Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles. , 2014, Healthcare technology letters.
[88] Feng Wan,et al. Adaptive Fourier decomposition based ECG denoising , 2016, Comput. Biol. Medicine.
[89] Pablo Laguna,et al. Block adaptive filters with deterministic reference inputs for event-related signals: BLMS and BRLS , 2002, IEEE Trans. Signal Process..
[90] María Eugenia Torres,et al. Improved complete ensemble EMD: A suitable tool for biomedical signal processing , 2014, Biomed. Signal Process. Control..
[91] S. Poornachandra,et al. Wavelet-based denoising using subband dependent threshold for ECG signals , 2008, Digit. Signal Process..
[92] Hongxin Zhang,et al. Wavelet De-Noising and Genetic Algorithm-Based Least Squares Twin SVM for Classification of Arrhythmias , 2017, Circuits Syst. Signal Process..
[93] Li Liu,et al. Adversarial de-noising of electrocardiogram , 2019, Neurocomputing.
[94] Patrick E. McSharry,et al. A dynamical model for generating synthetic electrocardiogram signals , 2003, IEEE Transactions on Biomedical Engineering.