Current methods in electrocardiogram characterization

The Electrocardiogram (ECG) is the P-QRS-T wave depicting the cardiac activity of the heart. The subtle changes in the electric potential patterns of repolarization and depolarization are indicative of the disease afflicting the patient. These clinical time domain features of the ECG waveform can be used in cardiac health diagnosis. Due to the presence of noise and minute morphological parameter values, it is very difficult to identify the ECG classes accurately by the naked eye. Various computer aided cardiac diagnosis (CACD) systems, analysis methods, challenges addressed and the future of cardiovascular disease screening are reviewed in this paper. Methods developed for time domain, frequency transform domain, and time-frequency domain analysis, such as the wavelet transform, cannot by themselves represent the inherent distinguishing features accurately. Hence, nonlinear methods which can capture the small variations in the ECG signal and provide improved accuracy in the presence of noise are discussed in greater detail in this review. A CACD system exploiting these nonlinear features can help clinicians to diagnose cardiovascular disease more accurately.

[1]  VASSILIS S. KODOGIANNIS,et al.  A Clustering-Based Fuzzy Wavelet Neural Network Model for Short-Term Load Forecasting , 2013, Int. J. Neural Syst..

[2]  U. Rajendra Acharya,et al.  Plaque Tissue Characterization and Classification in Ultrasound Carotid Scans: A Paradigm for Vascular Feature Amalgamation , 2013, IEEE Transactions on Instrumentation and Measurement.

[3]  Wei-Yen Hsu,et al.  Single-Trial Motor Imagery Classification using Asymmetry Ratio, phase Relation, Wavelet-Based Fractal, and their Selected Combination , 2013, Int. J. Neural Syst..

[4]  Hojjat Adeli,et al.  Comparison of fuzzy-wavelet radial basis function neural network freeway incident detection model with California algorithm , 2002 .

[5]  S. Sitharama Iyengar,et al.  Classification of heart rate data using artificial neural network and fuzzy equivalence relation , 2003, Pattern Recognit..

[6]  U. Rajendra Acharya,et al.  Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework , 2013, Knowl. Based Syst..

[7]  P. Vaidyanathan.  Multirate Systems And Filter Banks , 1992 .

[8]  Truong Q. Nguyen,et al.  Wavelets and filter banks , 1996 .

[9]  Yakup Kutlu,et al.  A multi-stage automatic arrhythmia recognition and classification system , 2011, Comput. Biol. Medicine.

[10]  俊一 甘利,et al.  A. Hyvärinen, J. Karhunen and E. Oja, Independent Component Analysis, Jhon Wiley & Sons, 2001年，504ページ． （根本幾・川勝真喜訳：独立成分分析——信号解析の新しい世界，東京電機大学出版局，2005年，532ページ．） , 2010 .

[11]  Kang-Ping Lin,et al.  QRS feature extraction using linear prediction , 1989, IEEE Transactions on Biomedical Engineering.

[12]  George Carayannis,et al.  QRS detection through time recursive prediction techniques , 1988 .

[13]  A Karim,et al.  COMPARISON OF THE FUZZY–WAVELET RBFNN FREEWAY INCIDENT DETECTION MODEL WITH THE CALIFORNIA ALGORITHM , 2002 .

[14]  G. Boudreaux-Bartels,et al.  Wavelet transform-based QRS complex detector , 1999, IEEE Transactions on Biomedical Engineering.

[15]  Bogdan Codres,et al.  Nonlinear System Identification and Control Based on Modular Neural Networks , 2011, Int. J. Neural Syst..

[16]  F. Minhas,et al.  Robust electrocardiogram (ECG) beat classification using discrete wavelet transform , 2008, Physiological measurement.

[17]  F. J. Fernández,et al.  ECG Characterization of paroxysmal atrial fibrillation: parameter extraction and automatic diagnosis algorithm , 2004, Comput. Biol. Medicine.

[18]  U. Rajendra Acharya,et al.  Automatic identification of cardiac health using modeling techniques: A comparative study , 2008, Inf. Sci..

[19]  Ulrik Söderström,et al.  Reconstruction of occluded facial images using asymmetrical Principal Component Analysis , 2011, 2011 18th International Conference on Systems, Signals and Image Processing.

[20]  Hojjat Adeli,et al.  HeartSaver: A mobile cardiac monitoring system for auto-detection of atrial fibrillation, myocardial infarction, and atrio-ventricular block , 2011, Comput. Biol. Medicine.

[21]  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.

[22]  Yüksel Özbay,et al.  A fuzzy clustering neural network architecture for classification of ECG arrhythmias , 2006, Comput. Biol. Medicine.

[23]  P. Wolf,et al.  Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. , 1991, Stroke.

[24]  U. Rajendra Acharya,et al.  Heart rate variability: a review , 2006, Medical and Biological Engineering and Computing.

[25]  Hojjat Adeli,et al.  Wavelet Packet‐Autocorrelation Function Method for Traffic Flow Pattern Analysis , 2004 .

[26]  Mang I Vai,et al.  Beat-to-beat ECG ventricular late potentials variance detection by filter bank and wavelet transform as beat-sequence filter , 2004, IEEE Transactions on Biomedical Engineering.

[27]  Mohammad Bagher Shamsollahi,et al.  Robust Detection of Premature Ventricular Contractions Using a Wave-Based Bayesian Framework , 2010, IEEE Transactions on Biomedical Engineering.

[28]  Roger G. Mark,et al.  The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it , 1990, [1990] Proceedings Computers in Cardiology.

[29]  David Dagan Feng,et al.  SparkMed: A Framework for Dynamic Integration of Multimedia Medical Data Into Distributed m-Health Systems , 2012, IEEE Transactions on Information Technology in Biomedicine.

[30]  Mark D. Huffman,et al.  Heart disease and stroke statistics--2013 update: a report from the American Heart Association. , 2013, Circulation.

[31]  P.E. Trahanias,et al.  An approach to QRS complex detection using mathematical morphology , 1993, IEEE Transactions on Biomedical Engineering.

[32]  N.V. Thakor,et al.  Multiway sequential hypothesis testing for tachyarrhythmia discrimination , 1994, IEEE Transactions on Biomedical Engineering.

[33]  Jun Cheng,et al.  A Wearable Smartphone-Based Platform for Real-Time Cardiovascular Disease Detection Via Electrocardiogram Processing , 2010, IEEE Transactions on Information Technology in Biomedicine.

[34]  M.S. Pattichis,et al.  m-Health e-Emergency Systems: Current Status and Future Directions [Wireless corner] , 2007, IEEE Antennas and Propagation Magazine.

[35]  G.G. Cano,et al.  An approach to cardiac arrhythmia analysis using hidden Markov models , 1990, IEEE Transactions on Biomedical Engineering.

[36]  Arik Wolak,et al.  Accuracy of Diagnosing Atrial Flutter and Atrial Fibrillation From a Surface Electrocardiogram by Hospital Physicians: Analysis of Data From Internal Medicine Departments , 2010, The American journal of the medical sciences.

[37]  Hojjat Adeli,et al.  Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection , 2008, IEEE Transactions on Biomedical Engineering.

[38]  Wei Wang,et al.  Structural Reliability Assessment by Local Approximation of Limit State Functions Using Adaptive Markov Chain Simulation and Support Vector Regression , 2012, Comput. Aided Civ. Infrastructure Eng..

[39]  S. Luo,et al.  A review of electrocardiogram filtering. , 2010, Journal of electrocardiology.

[40]  Sankaran Mahadevan,et al.  Bayesian wavelet packet denoising for structural system identification , 2007 .

[41]  A. Abdolahi Rad,et al.  Wavelet PSO‐Based LQR Algorithm for Optimal Structural Control Using Active Tuned Mass Dampers , 2013, Comput. Aided Civ. Infrastructure Eng..

[42]  Jerry M. Mendel,et al.  Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications , 1991, Proc. IEEE.

[43]  S Akselrod,et al.  Cross-correlation technique for arrhythmia detection using PR and PP intervals. , 1985, Computers and biomedical research, an international journal.

[44]  Enrico Zio,et al.  Genetic algorithm-based wrapper approach for grouping condition monitoring signals of nuclear power plant components , 2011, Integr. Comput. Aided Eng..

[45]  R. Poli,et al.  Genetic design of optimum linear and nonlinear QRS detectors , 1995, IEEE Transactions on Biomedical Engineering.

[46]  Sung-Nien Yu,et al.  Subband Features Based on Higher Order Statistics for ECG Beat Classification , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[48]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[49]  I.Y. Kim,et al.  Hierarchical support vector machine based heartbeat classification using higher order statistics and hermite basis function , 2008, 2008 Computers in Cardiology.

[50]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.

[51]  Specht Df,et al.  Clinical evaluation of a new computerized arrhythmia monitoring system. , 1975 .

[52]  R A Bruce,et al.  Computer-aided diagnosis of cardiovascular disorders. , 1966, Journal of chronic diseases.

[53]  Hojjat Adeli,et al.  Dynamic fuzzy wavelet neuroemulator for non‐linear control of irregular building structures , 2008 .

[54]  Hojjat Adeli,et al.  Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems , 1994 .

[55]  Thomas W. Rauber,et al.  Diagnosing multiple faults in oil rig motor pumps using support vector machine classifier ensembles , 2011, Integr. Comput. Aided Eng..

[56]  Paul S. Addison,et al.  The Illustrated Wavelet Transform Handbook , 2002 .

[57]  Elif Derya Übeyli,et al.  Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents , 2009, Comput. Methods Programs Biomed..

[58]  Ahmed Bouridane,et al.  2D and 3D palmprint information, PCA and HMM for an improved person recognition performance , 2013, Integr. Comput. Aided Eng..

[59]  Chandan Chakraborty,et al.  Application of Higher Order cumulant Features for Cardiac Health Diagnosis using ECG signals , 2013, Int. J. Neural Syst..

[60]  X Ning,et al.  Nonlinear dynamic characteristics analysis of synchronous 12-lead ECG signals. , 2000, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[61]  Chandan Chakraborty,et al.  Application of higher order cumulants to ECG signals for the cardiac health diagnosis , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[62]  Szi-Wen Chen,et al.  A two-stage discrimination of cardiac arrhythmias using a total least squares-based Prony modeling algorithm , 2000, IEEE Trans. Biomed. Eng..

[63]  Chandan Chakraborty,et al.  Automated Screening of Arrhythmia Using Wavelet Based Machine Learning Techniques , 2012, Journal of Medical Systems.

[64]  Hlaing Minn,et al.  A Patient-Adaptive Profiling Scheme for ECG Beat Classification , 2010, IEEE Transactions on Information Technology in Biomedicine.

[65]  U. Rajendra Acharya,et al.  Application of Empirical Mode Decomposition (EMD) for Automated Detection of epilepsy using EEG signals , 2012, Int. J. Neural Syst..

[66]  Stanislaw Osowski,et al.  Support vector machine-based expert system for reliable heartbeat recognition , 2004, IEEE Transactions on Biomedical Engineering.

[67]  Bernadette Dorizzi,et al.  Incremental HMM training applied to ECG signal analysis , 2008, Comput. Biol. Medicine.

[68]  Paul S Addison,et al.  Wavelet transforms and the ECG: a review , 2005, Physiological measurement.

[69]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[70]  Dimitris C. Theodoridis,et al.  Dynamical Recurrent Neuro-Fuzzy Identification Schemes Employing Switching parameter Hopping , 2012, Int. J. Neural Syst..

[71]  W.J. Tompkins,et al.  ECG beat detection using filter banks , 1999, IEEE Transactions on Biomedical Engineering.

[72]  Moncef Gabbouj,et al.  A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals , 2009, IEEE Transactions on Biomedical Engineering.

[73]  H. Huikuri,et al.  Sudden death due to cardiac arrhythmias. , 2001, The New England journal of medicine.

[74]  Sung-Nien Yu,et al.  Integration of independent component analysis and neural networks for ECG beat classification , 2008, Expert Syst. Appl..

[75]  E. Oja,et al.  Independent Component Analysis , 2013 .

[76]  C. Chiou,et al.  Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals , 2009 .

[77]  Sung-Nien Yu,et al.  Noise-tolerant electrocardiogram beat classification based on higher order statistics of subband components , 2009, Artif. Intell. Medicine.

[78]  Gustavo Belforte,et al.  A Contribution to the Automatic Processing of Electrocardiograms Using Syntactic Methods , 1979, IEEE Transactions on Biomedical Engineering.

[79]  Witold Pedrycz,et al.  A genetic segmentation of ECG signals , 2003, IEEE Transactions on Biomedical Engineering.

[80]  Willis J. Tompkins,et al.  Automated High-Speed Analysis of Holter Tapes with Microcomputers , 1983, IEEE Transactions on Biomedical Engineering.

[81]  Ataollah Ebrahimzadeh,et al.  Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features , 2010, Biomed. Signal Process. Control..

[82]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[83]  Conor Heneghan,et al.  Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea , 2003, IEEE Transactions on Biomedical Engineering.

[84]  H. K. Verma,et al.  ANN-based QRS-complex analysis of ECG. , 1998, Journal of medical engineering & technology.

[85]  James A. Reggia,et al.  Causally-guided evolutionary optimization and its application to antenna array design , 2012, Integr. Comput. Aided Eng..

[86]  Franco Bontempi,et al.  Genetic Algorithms for the Dependability Assurance in the Design of a Long‐Span Suspension Bridge , 2012, Comput. Aided Civ. Infrastructure Eng..

[87]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[88]  W.J. Tompkins,et al.  A patient-adaptable ECG beat classifier using a mixture of experts approach , 1997, IEEE Transactions on Biomedical Engineering.

[89]  Philip de Chazal,et al.  A Patient-Adapting Heartbeat Classifier Using ECG Morphology and Heartbeat Interval Features , 2006, IEEE Transactions on Biomedical Engineering.

[90]  Luca Citi,et al.  A Real-Time Automated Point-Process Method for the Detection and Correction of Erroneous and Ectopic Heartbeats , 2012, IEEE Transactions on Biomedical Engineering.

[91]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

[92]  Carsten Peterson,et al.  Clustering ECG complexes using Hermite functions and self-organizing maps , 2000, IEEE Trans. Biomed. Eng..

[93]  Gregory T. A. Kovacs,et al.  Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features , 2006, IEEE Transactions on Biomedical Engineering.

[94]  Kemal Polat,et al.  Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine , 2007, Appl. Math. Comput..

[95]  Ming Liang,et al.  Wavelet‐Based Detection of Beam Cracks Using Modal Shape and Frequency Measurements , 2012, Comput. Aided Civ. Infrastructure Eng..

[96]  Ljupco Hadzievski,et al.  A novel mobile transtelephonic system with synthesized 12-lead ECG , 2004, IEEE Transactions on Information Technology in Biomedicine.

[97]  Kayvan Najarian,et al.  Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning , 2012, BMC Medical Informatics and Decision Making.

[98]  Paul S. Addison,et al.  The Illustrated Wavelet Transform Handbook Introductory Theory And Applications In Science , 2002 .

[99]  Yakup Kutlu,et al.  Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients , 2012, Comput. Methods Programs Biomed..

[100]  R. Acharya U,et al.  Comprehensive analysis of cardiac health using heart rate signals , 2004, Physiological measurement.

[101]  C. M. Lim,et al.  Application of higher order statistics/spectra in biomedical signals--a review. , 2010, Medical engineering & physics.

[102]  Albert L. Waldo,et al.  Inter-relationships of atrial fibrillation and atrial flutter mechanisms and clinical implications. , 2008, Journal of the American College of Cardiology.

[103]  C. M. Lim,et al.  Automatic identification of epileptic electroencephalography signals using higher-order spectra , 2009, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[104]  Xiaojun Yuan,et al.  Multiple Functional ECG Signal is Processing for Wearable Applications of Long-Term Cardiac Monitoring , 2011, IEEE Transactions on Biomedical Engineering.

[105]  R. Orglmeister,et al.  The principles of software QRS detection , 2002, IEEE Engineering in Medicine and Biology Magazine.

[106]  Ali Cinar,et al.  Interpreting ECG data by integrating statistical and artificial intelligence tools. , 2002, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[107]  L LongoDan,et al.  Harrisons principles of internal medicine , 2013 .

[108]  John G. Proakis,et al.  Digital Signal Processing: Principles, Algorithms, and Applications , 1992 .

[109]  C. M. Lim,et al.  Classification of cardiac abnormalities using heart rate signals , 2004, Medical and Biological Engineering and Computing.

[110]  Madhuchhanda Mitra,et al.  Empirical mode decomposition based ECG enhancement and QRS detection , 2012, Comput. Biol. Medicine.

[111]  Sugata Munshi,et al.  Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification. , 2010, Medical engineering & physics.

[112]  H. Adeli,et al.  Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification , 2006 .

[113]  J.N. Watson,et al.  Finding coordinated atrial activity during ventricular fibrillation using wavelet decomposition , 2002, IEEE Engineering in Medicine and Biology Magazine.

[114]  U. Rajendra Acharya,et al.  Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization , 2013, Comput. Methods Programs Biomed..

[115]  Chandan Chakraborty,et al.  Cardiac decision making using higher order spectra , 2013, Biomed. Signal Process. Control..

[116]  Ning Zhang,et al.  Detection of Nonlinear Interactions of EEG alpha Waves in the Brain by a New Coherence Measure and its Application to Epilepsy and Anti-Epileptic Drug Therapy , 2011, Int. J. Neural Syst..

[117]  S. Yusuf,et al.  Stemming the global tsunami of cardiovascular disease , 2011, The Lancet.

[118]  José García,et al.  Automatic detection of ST-T complex changes on the ECG using filtered RMS difference series: application to ambulatory ischemia monitoring , 2000, IEEE Transactions on Biomedical Engineering.

[119]  M Bahoura,et al.  DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis. , 1997, Computer methods and programs in biomedicine.

[120]  A. Goldberger.  Clinical Electrocardiography: A Simplified Approach , 1977 .

[121]  U. Rajendra Acharya,et al.  ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform , 2013, Biomed. Signal Process. Control..

[122]  Mats-Erik Nygards,et al.  Adaptive QRS Detection Based on Maximum A Posteriori Estimation , 1982, IEEE Transactions on Biomedical Engineering.

[123]  W J Tompkins,et al.  Applications of artificial neural networks for ECG signal detection and classification. , 1993, Journal of electrocardiology.

[124]  Chih-Min Lin,et al.  Adaptive Control for MIMO uncertain nonlinear Systems Using Recurrent Wavelet Neural Network , 2012, Int. J. Neural Syst..

[125]  C. L. Nikias,et al.  Signal processing with higher-order spectra , 1993, IEEE Signal Processing Magazine.

[126]  Emil Jovanov,et al.  Guest Editorial Introduction to the Special Section on M-Health: Beyond Seamless Mobility and Global Wireless Health-Care Connectivity , 2004, IEEE Transactions on Information Technology in Biomedicine.

[127]  U. Rajendra Acharya,et al.  An Accurate and Generalized Approach to Plaque Characterization in 346 Carotid Ultrasound Scans , 2012, IEEE Transactions on Instrumentation and Measurement.

[128]  Sung-Nien Yu,et al.  Selection of effective features for ECG beat recognition based on nonlinear correlations , 2012, Artif. Intell. Medicine.

[129]  Dimitris C. Theodoridis,et al.  Indirect Adaptive Control of nonlinear Systems Based on Bilinear Neuro-Fuzzy Approximation , 2013, Int. J. Neural Syst..

[130]  Jasjit S. Suri,et al.  Automated carotid intima media thickness for prediction of SYNTAX score in Japanese coronary artery disease patients , 2013 .

[131]  Sundaram Suresh,et al.  Human action recognition using Meta-Cognitive Neuro-Fuzzy Inference System , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[132]  Hojjat Adeli,et al.  Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant Analysis , 2000 .

[133]  Xin Jiang,et al.  Crack Detection from the Slope of the Mode Shape Using Complex Continuous Wavelet Transform , 2012, Comput. Aided Civ. Infrastructure Eng..

[134]  Meng Joo Er,et al.  A Novel Efficient Learning Algorithm for Self-Generating Fuzzy Neural Network with Applications , 2012, Int. J. Neural Syst..

[135]  J. Suri,et al.  Atherosclerotic risk stratification strategy for carotid arteries using texture-based features. , 2012, Ultrasound in medicine & biology.

[136]  Sanjiv M Narayan,et al.  Accurate ECG Diagnosis of Atrial Tachyarrhythmias Using Quantitative Analysis: A Prospective Diagnostic and Cost‐Effectiveness Study , 2010, Journal of cardiovascular electrophysiology.

[137]  C. Chakraborty,et al.  An Integrated ECG Feature Extraction Scheme Using PCA and Wavelet Transform , 2009, 2009 Annual IEEE India Conference.

[138]  Jing Wang,et al.  A wavelet-based particle swarm optimization algorithm for digital image watermarking , 2012, Integr. Comput. Aided Eng..

[139]  Chandan Chakraborty,et al.  Application of principal component analysis to ECG signals for automated diagnosis of cardiac health , 2012, Expert Syst. Appl..

[140]  José García,et al.  Remote processing server for ECG-based clinical diagnosis support , 2002, IEEE Transactions on Information Technology in Biomedicine.

[141]  F. Gritzali.  Towards a generalized scheme for QRS detection in ECG waveforms , 1988 .

[142]  N. Kannathal,et al.  Cardiac State Diagnosis using Adaptive Neuro-Fuzzy Technique , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[143]  H. T. Nagle,et al.  A comparison of the noise sensitivity of nine QRS detection algorithms , 1990, IEEE Transactions on Biomedical Engineering.

[144]  Stanislaw Osowski,et al.  ECG beat recognition using fuzzy hybrid neural network , 2001, IEEE Trans. Biomed. Eng..

[145]  U. Rajendra Acharya,et al.  Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment , 2013, Medical & Biological Engineering & Computing.

[146]  W.J. Tompkins,et al.  Neural-network-based adaptive matched filtering for QRS detection , 1992, IEEE Transactions on Biomedical Engineering.

[147]  Mehmet Engin,et al.  ECG beat classification using neuro-fuzzy network , 2004, Pattern Recognit. Lett..

[148]  Arkady Borisov,et al.  Ranking-Based Kernels in Applied Biomedical Diagnostics Using a Support Vector Machine , 2011, Int. J. Neural Syst..

[149]  Michael Miller,et al.  Cardiovascular disease: A global problem extending into the developing world. , 2009, World journal of cardiology.

[150]  Chandan Chakraborty,et al.  A two-stage mechanism for registration and classification of ECG using Gaussian mixture model , 2009, Pattern Recognit..

[151]  L. Sörnmo,et al.  Delineation of the QRS complex using the envelope of the e.c.g. , 1983, Medical and Biological Engineering and Computing.

[152]  Gerasimos G. Rigatos.  Adaptive fuzzy control for differentially flat MIMO nonlinear dynamical systems , 2013, Integr. Comput. Aided Eng..

[153]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[154]  Hojjat Adeli,et al.  NEURO-FUZZY LOGIC MODEL FOR FREEWAY WORK ZONE CAPACITY ESTIMATION , 2003 .

[155]  Andreas S. Panayides,et al.  m-Health e-Emergency Systems , 2015 .

[156]  Saeed Karimifard,et al.  A robust method for diagnosis of morphological arrhythmias based on Hermitian model of higher-order statistics , 2011, Biomedical engineering online.

[157]  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.

[158]  Liang-Yu Shyu,et al.  Using wavelet transform and fuzzy neural network for VPC detection from the holter ECG , 2004, IEEE Transactions on Biomedical Engineering.

[159]  M.M.A. Hadhoud,et al.  Computer Aided Diagnosis of Cardiac Arrhythmias , 2006, 2006 International Conference on Computer Engineering and Systems.

[160]  Toshiyo Tamura,et al.  Home geriatric physiological measurements , 2012, Physiological measurement.

[161]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .