Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review

Deep learning models have become a popular mode to classify electrocardiogram (ECG) data. Investigators have used a variety of deep learning techniques for this application. Herein, a detailed examination of deep learning methods for ECG arrhythmia detection is provided. Approaches used by investigators are examined, and their contributions to the field are detailed. For this purpose, journal papers have been surveyed according to the methods used. In addition, various deep learning models and experimental studies are described and discussed. A five-class ECG dataset containing 100,022 beats was then utilized for further analysis of deep learning techniques. The constructed models were examined with this dataset, and results are presented. This study therefore provides information concerning deep learning approaches used for arrhythmia classification, and suggestions for further research in this area.

[1]  Andrew Y. Ng,et al.  Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks , 2017, ArXiv.

[2]  Nouma Izeboudjen,et al.  A New Neural Network System for Arrhythmia's Classification , 1998, NC.

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

[4]  U. Rajendra Acharya,et al.  Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network , 2017, Inf. Sci..

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

[6]  François Chollet,et al.  Deep Learning with Python , 2017 .

[7]  U. Rajendra Acharya,et al.  A new approach for arrhythmia classification using deep coded features and LSTM networks , 2019, Comput. Methods Programs Biomed..

[8]  Nurettin Acir.  Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm , 2005, Neural Computing & Applications.

[9]  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.

[10]  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.

[11]  K.Q. Wang,et al.  Diagnosis of cardiac arrhythmia using kernel difference weighted KNN classifier , 2008, 2008 Computers in Cardiology.

[12]  U. Rajendra Acharya,et al.  Deep learning for healthcare applications based on physiological signals: A review , 2018, Comput. Methods Programs Biomed..

[13]  Pu Wang,et al.  LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classification , 2020, IEEE Transactions on Instrumentation and Measurement.

[14]  Masoumeh Haghpanahi,et al.  Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network , 2019, Nature Medicine.

[15]  Shuo Li,et al.  An Automatic Cardiac Arrhythmia Classification System With Wearable Electrocardiogram , 2018, IEEE Access.

[16]  Jian Wang,et al.  Patient-specific ECG classification by deeper CNN from generic to dedicated , 2018, Neurocomputing.

[17]  Jonathan Rubin,et al.  Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings. , 2018, Journal of electrocardiology.

[18]  Bin Yao,et al.  Atrial Fibrillation Detection Using an Improved Multi-Scale Decomposition Enhanced Residual Convolutional Neural Network , 2019, IEEE Access.

[19]  Xiaolong Zhai,et al.  Automated ECG Classification Using Dual Heartbeat Coupling Based on Convolutional Neural Network , 2018, IEEE Access.

[20]  Bin Yao,et al.  ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network , 2019, IEEE Access.

[21]  Yoshua Bengio,et al.  Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery , 2019, ArXiv.

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

[23]  Feng Jiang,et al.  Very deep feature extraction and fusion for arrhythmias detection , 2018, Neural Computing and Applications.

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

[25]  Peng Lu,et al.  An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset , 2019, Journal of healthcare engineering.

[26]  Jianqing Li,et al.  Patient-Specific Deep Architectural Model for ECG Classification , 2017, Journal of healthcare engineering.

[27]  Khashayar Khorasani,et al.  Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[28]  Ruxin Wang,et al.  Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network , 2020, Inf. Fusion.

[29]  Asadollah Shahbahrami,et al.  CLASSIFICATION OF ECG ARRHYTHMIAS USING DISCRETE WAVELET TRANSFORM AND NEURAL NETWORKS , 2012 .

[30]  A. Uyar,et al.  Arrhythmia Classification Using Serial Fusion of Support Vector Machines and Logistic Regression , 2007, 2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications.

[31]  Sadasivan Puthusserypady,et al.  A deep learning approach for real-time detection of atrial fibrillation , 2019, Expert Syst. Appl..

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

[33]  Wei Lu,et al.  Feature fusion for imbalanced ECG data analysis , 2018, Biomed. Signal Process. Control..

[34]  Majid Moavenian,et al.  A qualitative comparison of Artificial Neural Networks and Support Vector Machines in ECG arrhythmias classification , 2010, Expert Syst. Appl..

[35]  Amjed S. Al-Fahoum,et al.  A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques , 2005, IEEE Transactions on Biomedical Engineering.

[36]  Masun Nabhan Homsi,et al.  Cardiac arrhythmia detection from ECG combining convolutional and long short-term memory networks , 2017, 2017 Computing in Cardiology (CinC).

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

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

[39]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[40]  Henggui Zhang,et al.  Detecting atrial fibrillation by deep convolutional neural networks , 2018, Comput. Biol. Medicine.

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

[42]  Ye Li,et al.  Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings , 2018, IEEE Journal of Biomedical and Health Informatics.

[43]  U. Rajendra Acharya,et al.  Automated detection of atrial fibrillation using long short-term memory network with RR interval signals , 2018, Comput. Biol. Medicine.

[44]  Majid Moavenian,et al.  A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification , 2010, Expert Syst. Appl..

[45]  U. Rajendra Acharya,et al.  Automated heartbeat classification and detection of arrhythmia using optimal orthogonal wavelet filters , 2019, Informatics in Medicine Unlocked.

[46]  H. Nakajima,et al.  Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network , 1999, IEEE Transactions on Biomedical Engineering.

[47]  U. Rajendra Acharya,et al.  Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals , 2019, Comput. Biol. Medicine.

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

[49]  Kazim Hanbay.  Deep neural network based approach for ECG classification using hybrid differential features and active learning , 2019, IET Signal Process..

[50]  C Banupriya,et al.  Electrocardiogram Beat Classification using Probabilistic Neural Network , 2014 .

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

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

[53]  S. Yoo,et al.  Support Vector Machine Based Arrhythmia Classification Using Reduced Features , 2005 .

[54]  Jibin Wang,et al.  A deep learning approach for atrial fibrillation signals classification based on convolutional and modified Elman neural network , 2020, Future Gener. Comput. Syst..

[55]  Jing Jiang,et al.  A novel multi-module neural network system for imbalanced heartbeats classification , 2019, Expert Syst. Appl. X.

[56]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[57]  Hamido Fujita,et al.  Computer Aided detection for fibrillations and flutters using deep convolutional neural network , 2019, Inf. Sci..

[58]  Man-Wai Mak,et al.  Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks , 2019, IEEE Journal of Biomedical and Health Informatics.

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

[60]  Gavin Sim,et al.  Inter-patient ECG classification with convolutional and recurrent neural networks , 2018, Biocybernetics and Biomedical Engineering.

[61]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[62]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

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

[64]  Chen Hao,et al.  On Arrhythmia Detection by Deep Learning and Multidimensional Representation , 2019, ArXiv.

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