Heart arrhythmia diagnosis based on the combination of morphological, frequency and nonlinear features of ECG signals and metaheuristic feature selection algorithm

Abstract Cardiac arrhythmia disorder is known as one of the most common diseases in the world. Today, this disease is considered as the leading cause of death in industrial and semi-industrial societies. Various tools and methods have been developed to study the detection of heart diseases, based on analyzing the electrocardiogram (ECG) signal. Due to the simplicity and noninvasive nature, ECG signals are vastly used by physicians to determine the heart problems and abnormalities. In this paper, a computer-aided diagnosis (CAD) system is provided for the automated classification and accurate diagnosis of seven types of cardiac arrhythmias using the ECG signal. The basis of this method is using machine learning algorithms to classify normal rhythm and six abnormal cardiac functions. In the proposed method, after the pre-processing stage, the ECG signal is segmented, and various morphological characteristics, frequency domain features, and nonlinear indices are extracted for the ECG signal. Several metaheuristic optimization algorithms are used to remove redundant or irrelevant features and reduce the feature space dimension. These are used on the combination of the extracted features in which, non-dominated sorting genetic algorithm (NSGA II) as a multi-objective optimization algorithm has the best performance. Furthermore, various machine learning algorithms include k-nearest neighbor (KNN), feed-forward neural network (FF net), fitting neural network (Fit net), radial basis function neural network (RBFNN) and pattern recognition network (Pat net) are employed for the classification. The highest accuracy obtained based on ten-fold cross-validation from the FF net is 98.75%, demonstrates the efficiency of the proposed method and the achieved improvement compared to the other similar works with the same dataset. The combination of a vast and various features from morphology, frequency, and nonlinear characteristics to demonstrate the diverse aspects of ECG signals as well as employing a multi-objective meta-heuristic optimization algorithm for selecting the more correlated features are the main contributions of this study.

[1]  Miguel C. Soriano,et al.  Electrocardiogram Classification Using Reservoir Computing With Logistic Regression , 2015, IEEE Journal of Biomedical and Health Informatics.

[2]  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.

[3]  U. Rajendra Acharya,et al.  Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease , 2019, Expert Syst. J. Knowl. Eng..

[4]  Abdullah Al Mahmud,et al.  Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz’s and Higuchi’s Method Under Fractal Dimensions , 2016 .

[5]  Maarten De Vos,et al.  Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG , 2017, 2017 Computing in Cardiology (CinC).

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

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

[8]  U. Rajendra Acharya,et al.  Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats , 2018, Comput. Biol. Medicine.

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

[10]  Sung-Nien Yu,et al.  Selection of significant independent components for ECG beat classification , 2009, Expert Syst. Appl..

[11]  Adnan Acan,et al.  ECG classification using three-level fusion of different feature descriptors , 2018, Expert Syst. Appl..

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

[13]  Andrey Lutich.  PatterNet: a system to learn compact physical design pattern representations for pattern-based analytics , 2017 .

[14]  Thomas M. Deserno,et al.  Deep Learning on 1-D Biosignals: a Taxonomy-based Survey , 2018, Yearbook of Medical Informatics.

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

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

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

[18]  Mohammad Ataei,et al.  Applying a modified version of Lyapunov exponent for cancer diagnosis in biomedical images: the case of breast mammograms , 2018, Multidimens. Syst. Signal Process..

[19]  José Carlos Príncipe,et al.  Time-Based Compression and Classification of Heartbeats , 2012, IEEE Transactions on Biomedical Engineering.

[20]  Abdel-Badeeh M. Salem,et al.  Intelligent hybrid approaches for human ECG signals identification , 2018, Signal, Image and Video Processing.

[21]  Martin Jung,et al.  A Guided Hybrid Genetic Algorithm for Feature Selection with Expensive Cost Functions , 2013, ICCS.

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

[23]  N. Ch. Sriman Narayana Iyengar,et al.  Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease , 2017, Comput. Biol. Medicine.

[24]  U. RajendraAcharya.  Advances in cardiac signal processing , 2007 .

[25]  Ram Bilas Pachori,et al.  Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform , 2018 .

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

[27]  U. Rajendra Acharya,et al.  A new machine learning technique for an accurate diagnosis of coronary artery disease , 2019, Comput. Methods Programs Biomed..

[28]  Adel Sabry Essa,et al.  Features Optimization for ECG Signals Classification , 2018 .

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

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

[31]  Hamed Khodadadi,et al.  Fuzzy controller design for breast cancer treatment based on fractal dimension using breast thermograms. , 2019, IET systems biology.

[32]  Yu-Liang Hsu,et al.  ECG arrhythmia classification using a probabilistic neural network with a feature reduction method , 2013, Neurocomputing.

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

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

[35]  Mohsen Ahmadi,et al.  Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals , 2018, Sensors.

[36]  Hossam Mosbah,et al.  Hourly Electricity Price Forecasting for the Next Month Using Multilayer Neural Network , 2016, Canadian Journal of Electrical and Computer Engineering.

[37]  Naif Alajlan,et al.  A wavelet optimization approach for ECG signal classification , 2012, Biomed. Signal Process. Control..

[38]  Rahime Ceylan,et al.  The Effect of Feature Extraction Based on Dictionary Learning on ECG Signal Classification , 2018 .

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

[40]  Xiaohe Chen,et al.  Arrhythmia Recognition and Classification Using ECG Morphology and Segment Feature Analysis , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[41]  U. Rajendra Acharya,et al.  Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals , 2017, Inf. Sci..

[42]  Hassan Ghassemian,et al.  Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal , 2012, Comput. Methods Programs Biomed..

[43]  A. Rastegari,et al.  Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal. , 2019, IET systems biology.

[44]  Farid Melgani,et al.  Genetic algorithm-based method for mitigating label noise issue in ECG signal classification , 2015, Biomed. Signal Process. Control..

[45]  Mehmet Korürek,et al.  ECG heart beat classification method based on modified ABC algorithm , 2015, Appl. Soft Comput..

[46]  Gaetano D Gargiulo,et al.  Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method , 2019, Sensors.

[47]  Mohsen Nabian,et al.  An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data , 2018, IEEE Journal of Translational Engineering in Health and Medicine.

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

[49]  A. Sedigh,et al.  Nonlinear Analysis of the Contour Boundary Irregularity of Skin Lesion Using Lyapunov Exponent and K-S Entropy , 2017 .

[50]  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..

[51]  Marta Borowska,et al.  Entropy-Based Algorithms in the Analysis of Biomedical Signals , 2015 .

[52]  Sengul Dogan,et al.  Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals , 2019, Knowl. Based Syst..