Novel Methodology for Cardiac Arrhythmias Classification Based on Long-Duration ECG Signal Fragments Analysis

According to the reports published by various organizations, it can be seen that about 50 million people are at risk of cardiovascular diseases (CVDs) around the world. Moreover, different types of heart diseases are the most common causes of mortality. This chapter, therefore, investigates a cardiac disorders database (ECG) with 17 classes (normal sinus rhythm, the rhythm of the pacemaker, and fifteen arrhythmias) using a novel classification methodology. The data set is based on long-duration ECG signal fragments. The Electrocardiography (ECG) is a very popular process to record the electrical activity of the heart during specific time. Even though there are a lot of studies in the literature, however, there are many other open issues in the topic. The main objective of the current study is to present a new and efficient methods in order to do automatic recognition of myocardium dysfunctions. The proposed methods are introduced that can be used in different situations such as mobile devices, telemedicine, cloud computing, and finally supporting preventive and supportive treatment of CVDs. Since the performance of proposed algorithms is very important, the time duration, as an additional criterion, is also analyzed in real time. The obtained outcomes demonstrate that our methodology has outstanding performance compared to the methods presented in the literature. This study uses 744 fragments of ECG signal database related to 29 patients from the MIH-BIH Arrhythmia database (only for one lead—MLII). By using Welch’s method and a discrete Fourier transform, the spectral power density is predicted in order to increase the characteristic features of the ECG signals. The research presents a new evolutionary-neural system, based on the SVM classifier. The proposed method shows good performance with high sensitivity (90.19%), specificity (99.39%), and accuracy (98.85%).

[1]  Chun-Cheng Lin,et al.  Heartbeat Classification Using Normalized RR Intervals and Wavelet Features , 2014, 2014 International Symposium on Computer, Consumer and Control.

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[4]  Poonam Zham,et al.  Efficacy of Guided Spiral Drawing in the Classification of Parkinson's Disease , 2018, IEEE Journal of Biomedical and Health Informatics.

[5]  B. V. K. Vijaya Kumar,et al.  Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals , 2012, IEEE Transactions on Biomedical Engineering.

[6]  B. V. K. Vijaya Kumar,et al.  Combining general multi-class and specific two-class classifiers for improved customized ECG heartbeat classification , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[7]  Diego Mendez,et al.  Wide Machine Learning Algorithms Evaluation Applied to ECG Authentication and Gender Recognition , 2018, ICBEA '18.

[8]  Germán Castellanos-Domínguez,et al.  Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering , 2012, Comput. Methods Programs Biomed..

[9]  Dinesh K Kumar,et al.  Hybrid independent component analysis and twin support vector machine learning scheme for subtle gesture recognition , 2010, Biomedizinische Technik. Biomedical engineering.

[10]  Jimmy Xiangji Huang,et al.  Learning to rank diversified results for biomedical information retrieval from multiple features , 2014, Biomedical engineering online.

[11]  Naomie Salim,et al.  Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals , 2016, Comput. Methods Programs Biomed..

[12]  Abdulhamit Subasi,et al.  Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases , 2015, Circuits Syst. Signal Process..

[13]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[14]  Robert Nadon,et al.  Detecting and removing multiplicative spatial bias in high‐throughput screening technologies , 2017, Bioinform..

[15]  U. Rajendra Acharya,et al.  A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals , 2017, Applied Intelligence.

[16]  Pawe Pawiak,et al.  Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system , 2018 .

[17]  U. Rajendra Acharya,et al.  A novel machine learning approach for early detection of hepatocellular carcinoma patients , 2019, Cognitive Systems Research.

[18]  U. Rajendra Acharya,et al.  Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types , 2019, Comput. Biol. Medicine.

[19]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[20]  Ke-Ji Chen,et al.  Sodium Tanshinone II A Sulfonate for Coronary Heart Disease: A Systematic Review of Randomized Controlled Trials , 2018, Chinese Journal of Integrative Medicine.

[21]  Vladimir Makarenkov,et al.  A new fast method for inferring multiple consensus trees using k-medoids , 2018, BMC Evolutionary Biology.

[22]  An-Yeu Wu,et al.  Low-Complexity Privacy-Preserving Compressive Analysis Using Subspace-Based Dictionary for ECG Telemonitoring System , 2018, IEEE Transactions on Biomedical Circuits and Systems.

[23]  Özal Yildirim,et al.  A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification , 2018, Comput. Biol. Medicine.

[24]  Guixia Kang,et al.  Breast Cancer Classification Based on Fully-Connected Layer First Convolutional Neural Networks , 2018, IEEE Access.

[25]  Ganesh R. Naik,et al.  Detection of gait initiation Failure in Parkinson's disease based on wavelet transform and Support Vector Machine , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[26]  Jinsul Kim,et al.  An Automated ECG Beat Classification System Using Convolutional Neural Networks , 2016, 2016 6th International Conference on IT Convergence and Security (ICITCS).

[27]  Ryszard Tadeusiewicz,et al.  Neural networks as a tool for modeling of biological systems , 2015, Bio Algorithms Med Syst..

[28]  Ramesh Sharda,et al.  Sensor analytics for interpretation of EKG signals , 2019, Expert Syst. Appl..

[29]  Jeffrey M. Hausdorff,et al.  Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson’s Disease , 2018, Scientific Reports.

[30]  Michel Verleysen,et al.  Weighted SVMs and Feature Relevance Assessment in Supervised Heart Beat Classification , 2010, BIOSTEC.

[31]  David Menotti,et al.  ECG arrhythmia classification based on optimum-path forest , 2013, Expert Syst. Appl..

[32]  S P Arjunan,et al.  A machine learning based method for classification of fractal features of forearm sEMG using Twin Support vector machines , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[33]  Ozal Yildirim,et al.  ECG BEAT DETECTION AND CLASSIFICATION SYSTEM USING WAVELET TRANSFORM AND ONLINE SEQUENTIAL ELM , 2019, Journal of Mechanics in Medicine and Biology.

[34]  U. Rajendra Acharya,et al.  Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals , 2019, Neural Computing and Applications.

[35]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[36]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[37]  Di Wang,et al.  Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine , 2018, Comput. Biol. Medicine.

[38]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[39]  Juan Pablo Martínez,et al.  Heartbeat Classification Using Feature Selection Driven by Database Generalization Criteria , 2011, IEEE Transactions on Biomedical Engineering.

[40]  U. Rajendra Acharya,et al.  Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study , 2017, Inf. Sci..

[41]  Naif Alajlan,et al.  Domain adaptation methods for ECG classification , 2013, 2013 International Conference on Computer Medical Applications (ICCMA).

[42]  Giuseppe De Pietro,et al.  A deep learning approach for ECG-based heartbeat classification for arrhythmia detection , 2018, Future Gener. Comput. Syst..

[43]  U. Rajendra Acharya,et al.  Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation , 2014, Biomed. Signal Process. Control..

[44]  Aggelos K. Katsaggelos,et al.  ECG-Based Classification of Resuscitation Cardiac Rhythms for Retrospective Data Analysis , 2017, IEEE Transactions on Biomedical Engineering.

[45]  Moloud Abdar,et al.  Rule Optimization of Boosted C5.0 Classification Using Genetic Algorithm for Liver disease Prediction , 2017, 2017 International Conference on Computer and Applications (ICCA).

[46]  Xiaoqing Luo,et al.  Heartbeat classification using decision level fusion , 2014 .

[47]  Vladimir Makarenkov,et al.  Towards an accurate identification of mosaic genes and partial horizontal gene transfers , 2011, Nucleic acids research.

[48]  Abdulhamit Subasi,et al.  Breast cancer diagnosis using GA feature selection and Rotation Forest , 2015, Neural Computing and Applications.

[49]  Philip de Chazal,et al.  Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[50]  Noor Azuan Abu Osman,et al.  Evaluation of new suspension system for limb prosthetics , 2014, Biomedical engineering online.

[51]  Vladimir Makarenkov,et al.  Modeling functional specialization of a cell colony under different fecundity and viability rates and resource constraint , 2018, PloS one.

[52]  Steven B. Smith,et al.  Digital Signal Processing: A Practical Guide for Engineers and Scientists , 2002 .

[53]  Madhuchhanda Mitra,et al.  Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification , 2014, IEEE Transactions on Instrumentation and Measurement.

[54]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[55]  Gunasekaran Manogaran,et al.  RETRACTED ARTICLE: A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing , 2017, Multimedia Tools and Applications.

[56]  Stephen L. Smith,et al.  Using echo state networks for classification: A case study in Parkinson's disease diagnosis , 2018, Artif. Intell. Medicine.

[57]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[58]  Leszek Rutkowski,et al.  Computational intelligence - methods and techniques , 2008 .

[59]  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.

[60]  Hirokazu Takahashi,et al.  A Data Mining-based Prognostic Algorithm for NAFLD-related Hepatoma Patients: A Nationwide Study by the Japan Study Group of NAFLD , 2018, Scientific Reports.

[61]  Dursun Delen,et al.  Predicting breast cancer survivability: a comparison of three data mining methods , 2005, Artif. Intell. Medicine.

[62]  J P Martinez,et al.  Analysis of multidomain features for ECG classification , 2009, 2009 36th Annual Computers in Cardiology Conference (CinC).

[63]  Ajith Abraham,et al.  Optimal breast cancer classification using Gauss-Newton representation based algorithm , 2017, Expert Syst. Appl..

[64]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[65]  Nello Cristianini,et al.  Support Vector Machines and Kernel Methods: The New Generation of Learning Machines , 2002, AI Mag..

[66]  Joel E. W. Koh,et al.  Entropies for automated detection of coronary artery disease using ECG signals: A review , 2018 .

[67]  William Robson Schwartz,et al.  ECG-based heartbeat classification for arrhythmia detection: A survey , 2016, Comput. Methods Programs Biomed..

[68]  Sebastian Zaunseder,et al.  Optimization of ECG Classification by Means of Feature Selection , 2011, IEEE Transactions on Biomedical Engineering.

[69]  Mohammad Ali Tinati,et al.  Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals , 2016, Pattern Recognit. Lett..

[70]  Kup-Sze Choi,et al.  Heartbeat classification using disease-specific feature selection , 2014, Comput. Biol. Medicine.

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

[72]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[73]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[74]  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..

[75]  Jian Li,et al.  Heartbeat classification using projected and dynamic features of ECG signal , 2017, Biomed. Signal Process. Control..

[76]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[77]  Pawel Plawiak,et al.  Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals , 2017, Swarm Evol. Comput..

[78]  Moloud Abdar,et al.  Impact of Patients’ Gender on Parkinson’s disease using Classification Algorithms , 2018 .

[79]  U. Rajendra Acharya,et al.  Arrhythmia detection using deep convolutional neural network with long duration ECG signals , 2018, Comput. Biol. Medicine.

[80]  Moloud Abdar,et al.  Performance analysis of classification algorithms on early detection of liver disease , 2017, Expert Syst. Appl..

[81]  Michel Verleysen,et al.  Weighted Conditional Random Fields for Supervised Interpatient Heartbeat Classification , 2012, IEEE Transactions on Biomedical Engineering.

[82]  Moloud Abdar,et al.  Improving the Diagnosis of Liver Disease Using Multilayer Perceptron Neural Network and Boosted Decision Trees , 2018 .

[83]  Huifang Huang,et al.  A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals , 2014, BioMedical Engineering OnLine.

[84]  U. Rajendra Acharya,et al.  A deep convolutional neural network model to classify heartbeats , 2017, Comput. Biol. Medicine.

[85]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[86]  Michio Sugeno,et al.  Industrial Applications of Fuzzy Control , 1985 .

[87]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[88]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[89]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.