ECG-based machine-learning algorithms for heartbeat classification

[1]  Mohamed-Slim Alouini,et al.  Fractional Fourier Transform Based QRS Complex Detection in ECG Signal , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Jun Zhu,et al.  Variations in common diseases, hospital admissions, and deaths in middle-aged adults in 21 countries from five continents (PURE): a prospective cohort study , 2020, The Lancet.

[3]  Cyril Rakovski,et al.  A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients , 2020, Scientific Data.

[4]  Hangyuan Guo,et al.  A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients , 2020, Scientific Data.

[5]  Célia Michotey,et al.  The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe , 2020, Scientific Data.

[6]  Monika Agrawal,et al.  Aiding the Detection of QRS Complex in ECG Signals by Detecting S Peaks Independently , 2018, Cardiovascular Engineering and Technology.

[7]  Kandala N. V. P. S. Rajesh,et al.  Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier , 2018, Biomed. Signal Process. Control..

[8]  Jichao Zhao,et al.  Robust ECG signal classification for detection of atrial fibrillation using a novel neural network , 2017, 2017 Computing in Cardiology (CinC).

[9]  Marcin Maciejewski,et al.  ECG parameter extraction and classification in noisy signals , 2017, 2017 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).

[10]  Mohamed Abid,et al.  Single Scale CWT Algorithm for ECG Beat Detection for a Portable Monitoring System , 2017 .

[11]  Mohamed Elgendi,et al.  TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach , 2016, Biosensors.

[12]  Derek Abbott,et al.  A Proof-of-Concept Study: Simple and Effective Detection of P and T Waves in Arrhythmic ECG Signals , 2016, Bioengineering.

[13]  Pornchai Phukpattaranont,et al.  Application of wavelet transform and Shannon energy on R peak detection algorithm , 2016, 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).

[14]  Shweta H. Jambukia,et al.  Classification of ECG signals using machine learning techniques: A survey , 2015, 2015 International Conference on Advances in Computer Engineering and Applications.

[15]  Fabio Del Frate,et al.  Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking , 2015, Remote. Sens..

[16]  Mounir Sayadi,et al.  R peak detection in electrocardiogram signal based on a combination between empirical mode decomposition and Hilbert transform , 2014, 2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).

[17]  R. Rajni,et al.  Electrocardiogram Signal Analysis - An Overview , 2013 .

[18]  Mohamed Elgendi,et al.  Fast QRS Detection with an Optimized Knowledge-Based Method: Evaluation on 11 Standard ECG Databases , 2013, PloS one.

[19]  Bingo Wing-Kuen Ling,et al.  Motion artifact suppression in the ECG signal by successive modifications in frequency and time , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  Murugappan Murugappan,et al.  ECG Signal Denoising Using Wavelet Thresholding Techniques in Human Stress Assessment , 2012 .

[21]  Jeong-A Lee,et al.  R-READER: A lightweight algorithm for rapid detection of ECG signal R-peaks , 2012, 2011 2nd International Conference on Engineering and Industries (ICEI).

[22]  L. Leija-Salas,et al.  ECG baseline drift removal using discrete wavelet transform , 2011, 2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control.

[23]  Shahanaz Ayub,et al.  ECG classification and abnormality detection using cascade forward neural network , 2011 .

[24]  LJubisa Stankovic,et al.  Fractional Fourier transform as a signal processing tool: An overview of recent developments , 2011, Signal Process..

[25]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[26]  Fawzi M. Al-Naima,et al.  Neural Network Based Classification of Myocardial Infarction: A Comparative Study of Wavelet and Fourier Transforms , 2009 .

[27]  Mohamed Elgendi,et al.  R wave detection using Coiflets wavelets , 2009, 2009 IEEE 35th Annual Northeast Bioengineering Conference.

[28]  Patrick E. McSharry,et al.  Advanced Methods And Tools for ECG Data Analysis , 2006 .

[29]  Liqing Zhang,et al.  ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines , 2005, 2005 International Conference on Neural Networks and Brain.

[30]  Massimiliano Pontil,et al.  Support Vector Machines: Theory and Applications , 2001, Machine Learning and Its Applications.

[31]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[32]  Arnold Neumaier,et al.  Algorithm 808: ARfit—a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models , 2001, TOMS.

[33]  Gozde Bozdagi Akar,et al.  Digital computation of the fractional Fourier transform , 1996, IEEE Trans. Signal Process..

[34]  Luís B. Almeida,et al.  The fractional Fourier transform and time-frequency representations , 1994, IEEE Trans. Signal Process..

[35]  S. Padmavathi,et al.  Naïve Bayes Classifier for ECG Abnormalities Using Multivariate Maximal Time Series Motif , 2015 .

[36]  Friso De Boer,et al.  Frequency Bands Effects on QRS Detection , 2010, BIOSIGNALS.

[37]  Zhou Min Digital Computation of Fractional Fourier Transform , 2002 .

[38]  Robert Plonsey,et al.  Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields , 1995 .

[39]  Kiran Ahuja,et al.  A novel approach for Extraction and Classification of ECG signal using SVM , 2022 .