Electrocardiogram soft computing using hybrid deep learning CNN-ELM

Abstract Electrocardiogram (ECG) can reflect the state of human heart and is widely used in clinical cardiac examination. However, the electrocardiogram signal is very weak, the anti-interference ability is poor, easy to be affected by the noise. Doctors face difficulties in diagnosing arrhythmias. Therefore, automatic recognition and classification of ECG signals is an important and indispensable task. Since the beginning of the 21 st century, deep learning has developed rapidly and has shown the most advanced performance in various fields. This paper presents a method of combining (Convolutional neural network) CNN and ELM (extreme learning machine). The accuracy rate is 97.50%. Compared with the state-of-the-art methods, this method improves the accuracy of ECG automatic classification and has good generalization ability.

[1]  Min Zhou,et al.  ECG Classification Using Wavelet Packet Entropy and Random Forests , 2016, Entropy.

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

[3]  Bapi Raju Surampudi,et al.  Inter-patient heart-beat classification using complete ECG beat time series by alignment of R-peaks using SVM and decision rule , 2016, 2016 International Conference on Signal and Information Processing (IConSIP).

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

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

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

[7]  Mohammad Sarfraz,et al.  Using independent component analysis to obtain feature space for reliable ECG Arrhythmia classification , 2014, 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

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

[9]  Chia-Hung Lin,et al.  Adaptive wavelet network for multiple cardiac arrhythmias recognition , 2008, Expert Syst. Appl..

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

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

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

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

[14]  Mohammad Bagher Shamsollahi,et al.  Multiadaptive Bionic Wavelet Transform: Application to ECG Denoising and Baseline Wandering Reduction , 2007, EURASIP J. Adv. Signal Process..

[15]  Jin Wang,et al.  Spatial and semantic convolutional features for robust visual object tracking , 2018, Multimedia Tools and Applications.

[16]  Naif Alajlan,et al.  Deep learning approach for active classification of electrocardiogram signals , 2016, Inf. Sci..

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

[18]  Fei Peng,et al.  Separable reversible data hiding and encryption for HEVC video , 2017, Journal of Real-Time Image Processing.

[19]  Zhenmin Tang,et al.  Sparse representation-based ECG signal enhancement and QRS detection , 2016, Physiological measurement.

[20]  Chun-Cheng Lin,et al.  Heartbeat Classification Using Normalized RR Intervals and Morphological Features , 2014 .

[21]  Sandeep Raj,et al.  ECG Signal Analysis Using DCT-Based DOST and PSO Optimized SVM , 2017, IEEE Transactions on Instrumentation and Measurement.

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

[23]  N H Lovell,et al.  Electrocardiogram signal quality measures for unsupervised telehealth environments , 2012, Physiological measurement.

[24]  Hye-Jin Kim,et al.  An Enhanced PEGASIS Algorithm with Mobile Sink Support for Wireless Sensor Networks , 2018, Wirel. Commun. Mob. Comput..

[25]  Moncef Gabbouj,et al.  Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Biomedical Engineering.

[26]  John M. Cioffi,et al.  ECG Enhancement by Adaptive Cancellation of Electrosurgical Interference , 1983, IEEE Transactions on Biomedical Engineering.

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

[28]  Yüksel Özbay,et al.  Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network , 2007, Expert Syst. Appl..

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

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

[31]  Elif Derya íbeyli.  Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals , 2010 .

[32]  R. Sherratt,et al.  Adversarial learning for distant supervised relation extraction , 2018 .

[33]  Francisco Javier de Cos Juez,et al.  Analysis of the High-Frequency Content in Human QRS Complexes by the Continuous Wavelet Transform: An Automatized Analysis for the Prediction of Sudden Cardiac Death , 2018, Sensors.

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

[35]  Cheng Wen,et al.  Classification of ECG complexes using self-organizing CMAC , 2009 .

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

[37]  Kamalesh Kumar Sharma,et al.  QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform , 2016 .

[38]  Tamer Ölmez,et al.  ECG beat classification by a novel hybrid neural network , 2001, Comput. Methods Programs Biomed..

[39]  Refet Firat Yazicioglu,et al.  ECG Signal Compression and Classification Algorithm With Quad Level Vector for ECG Holter System , 2010, IEEE Transactions on Information Technology in Biomedicine.

[40]  Mohammad Bagher Shamsollahi,et al.  ECG Denoising and Compression Using a Modified Extended Kalman Filter Structure , 2008, IEEE Transactions on Biomedical Engineering.

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

[42]  Yüksel Özbay,et al.  A new method for classification of ECG arrhythmias using neural network with adaptive activation function , 2010, Digit. Signal Process..

[43]  Jiaohua Qin,et al.  Discrete Multi-graph Hashing for Large-Scale Visual Search , 2018, Neural Processing Letters.

[44]  Earl R. Ferrara,et al.  Fetal Electrocardiogram Enhancement by Time-Sequenced Adaptive Filtering , 1982, IEEE Transactions on Biomedical Engineering.

[45]  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.

[46]  Yan Gui,et al.  Joint learning of visual and spatial features for edit propagation from a single image , 2019, The Visual Computer.

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

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

[49]  Diane Crawford,et al.  Editorial , 2000, CACM.

[50]  Xingming Sun,et al.  A robust forgery detection algorithm for object removal by exemplar-based image inpainting , 2017, Multimedia Tools and Applications.