A novel ECG signal classification method using DEA-ELM.

Electrocardiogram (ECG) signals represent the electrical mobility of the human heart. In recent years, computer-aided systems have helped to cardiologists in the detection, classification and diagnosis of ECG. The aim of this paper is to optimize the number hidden neurons of the traditional Extreme Learning Machine (ELM) using Differential Evolution Algorithm (DEA) and contribute to the classification of ECG signals with a higher accuracy rate. In this paper, publicly ECG records in Physionet was utilized. Pan-Tompkins technique (PTT) and Discrete Wavelet Transform (DWT) approaches were implemented to obtain characteristic properties which are PR period, QT period, ST period and QRS wave of ECG signals. Then, ELM was executed to the ECG samples. Lastly, DEA on software ELM was developed for the assign of the number of hidden neurons, which were used in the ELM algorithm. The performance criterions were used in order to compare the performance of the classification exerted. Concordantly, it was realized that the highest classification achievement values were reached to Accuracy 97.5% and values 93 of number of hidden neurons, with the practice improved with the DEA compared to conventional ELM.

[1]  Daeyoung Kim,et al.  ECG arrhythmia classification using a 2-D convolutional neural network , 2018, ArXiv.

[2]  Kwangsoo Shin,et al.  Robust algorithm for arrhythmia classification in ECG using extreme learning machine , 2009, Biomedical engineering online.

[3]  Monica Subashini,et al.  Analysis of Electrocardiograph (ECG) Signal for the Detection of Abnormalities Using MATLAB , 2014 .

[4]  Engin Avci,et al.  A new method for expert target recognition system: Genetic wavelet extreme learning machine (GAWELM) , 2013, Expert Syst. Appl..

[5]  Guang-Bin Huang,et al.  Extreme Learning Machine for Multilayer Perceptron , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Mehmet Korürek,et al.  ECG beat classification using particle swarm optimization and radial basis function neural network , 2010, Expert Syst. Appl..

[7]  Mehmet Korürek,et al.  Clustering MIT-BIH arrhythmias with Ant Colony Optimization using time domain and PCA compressed wavelet coefficients , 2010, Digit. Signal Process..

[8]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[9]  Farid Melgani,et al.  Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization , 2008, IEEE Transactions on Information Technology in Biomedicine.

[10]  Engin Avci,et al.  Determination of R-peaks in ECG signal using Hilbert Transform and Pan-Tompkins Algorithms , 2017, 2017 25th Signal Processing and Communications Applications Conference (SIU).

[11]  Engin Avci,et al.  A Diagnostic Model for Identification of Myocardial Infarction from Electrocardiography Signals , 2017 .

[12]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[13]  Sarajane Marques Peres,et al.  Feature selection for biometrie recognition based on electrocardiogram signals , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[14]  Engin Avci,et al.  A new automatic target recognition system based on wavelet extreme learning machine , 2012, Expert Syst. Appl..

[15]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Naif Alajlan,et al.  Classification of AAMI heartbeat classes with an interactive ELM ensemble learning approach , 2015, Biomed. Signal Process. Control..

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

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

[19]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[20]  S. P. Kulkarni.  DWT and ANN Based Heart Arrhythmia Disease Diagnosis from MIT-BIH ECG Signal Data , 2015 .

[21]  Ping Zhang,et al.  Ant Colony Optimization Based Memetic Algorithm to Solve Bi-Objective Multiple Traveling Salesmen Problem for Multi-Robot Systems , 2018, IEEE Access.

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

[23]  Oscar Castillo,et al.  Differential Evolution Algorithm , 2020 .

[24]  Engin Avci,et al.  White blood cells detection and classification based on regional convolutional neural networks. , 2019, Medical hypotheses.

[25]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[26]  Hassan Feshki Farahani,et al.  Optimal allocation of plug-in electric vehicle capacity to produce active, reactive and distorted powers using differential evolution based artificial bee colony algorithm , 2017 .

[27]  Selcan Kaplan Berkaya,et al.  A survey on ECG analysis , 2018, Biomed. Signal Process. Control..

[28]  Geetha D. Devanagavi,et al.  GB-SVNN: Genetic BAT assisted support vector neural network for arrhythmia classification using ECG signals , 2018, J. King Saud Univ. Comput. Inf. Sci..

[29]  Engin Avci,et al.  A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks , 2019, Sensors.

[30]  S. Karpagachelvi.  Classification of ECG signals using hybrid particle swarm optimization in extreme learning machine , 2014 .

[31]  Shailja Shukla,et al.  ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier , 2013 .

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

[33]  Douglas A. G. Vieira,et al.  Extreme learning machine with parallel layer perceptrons , 2015, Neurocomputing.

[34]  Ali Karci.  Differential Evolution Algorithm and Its Variants , 2017 .

[35]  Engin Avci,et al.  A Novel Approach in Analyzing Traffic Flow by Extreme Learning Machine Method , 2019, Tehnicki vjesnik - Technical Gazette.