Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats

Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats. Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) signal. However, it can be challenging and time-consuming to visually assess the ECG signals due to the very low amplitudes. Implementing an automated system in the clinical setting can potentially help expedite diagnosis of arrhythmia, and improve the accuracies. In this paper, we propose an automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature beats (APB) and premature ventricular contraction (PVC) on ECG signals. The novelty of this work is that we used ECG segments of variable length from the MIT-BIT arrhythmia physio bank database. The proposed system demonstrated high classification performance in the handling of variable-length data, achieving an accuracy of 98.10%, sensitivity of 97.50% and specificity of 98.70% using ten-fold cross validation strategy. Our proposed model can aid clinicians to detect common arrhythmias accurately on routine screening ECG.

[1]  Hermann Ney,et al.  From Feedforward to Recurrent LSTM Neural Networks for Language Modeling , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

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

[3]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Truyen Tran,et al.  Predicting healthcare trajectories from medical records: A deep learning approach , 2017, J. Biomed. Informatics.

[5]  Frank K. Soong,et al.  Effective Spectral and Excitation Modeling Techniques for LSTM-RNN-Based Speech Synthesis Systems , 2017, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[6]  Lianwen Jin,et al.  Improved deep convolutional neural network for online handwritten Chinese character recognition using domain-specific knowledge , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

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

[8]  Mark D. McDonnell,et al.  Understanding Data Augmentation for Classification: When to Warp? , 2016, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[9]  Douglas M. Hawkins,et al.  The Problem of Overfitting , 2004, J. Chem. Inf. Model..

[10]  Myungjong Kim,et al.  Speaker-Independent Silent Speech Recognition From Flesh-Point Articulatory Movements Using an LSTM Neural Network , 2017, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[11]  M J Walsh,et al.  Natural history of isolated bundle branch block. , 1996, The American journal of cardiology.

[12]  G Olivetti,et al.  Myocyte cell loss and myocyte cellular hyperplasia in the hypertrophied aging rat heart. , 1990, Circulation research.

[13]  Daniel L. Rubin,et al.  Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions , 2017, Journal of Digital Imaging.

[14]  Santanu Sahoo,et al.  Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities , 2017 .

[15]  C. Chiou,et al.  Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals , 2009 .

[16]  W. Kannel,et al.  Newly acquired right bundle-branch block: The Framingham Study. , 1979, Annals of internal medicine.

[17]  U. Rajendra Acharya,et al.  Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals , 2018, Comput. Biol. Medicine.

[18]  B. Hedblad,et al.  Cardiac Arrhythmias and Stroke: Increased Risk in Men With High Frequency of Atrial Ectopic Beats , 2000, Stroke.

[19]  G. Thorgeirsson,et al.  The epidemiology of right bundle branch block and its association with cardiovascular morbidity--the Reykjavik Study. , 1993, European heart journal.

[20]  Jamuna Kanta Sing,et al.  Convolution of 3D Gaussian surfaces for volumetric intensity inhomogeneity estimation and correction in 3D brain MR image data , 2017, IET Comput. Vis..

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

[22]  Chandan Chakraborty,et al.  Application of higher order cumulants to ECG signals for the cardiac health diagnosis , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Qing Li,et al.  Multiple VLAD Encoding of CNNs for Image Classification , 2017, Computing in Science & Engineering.

[24]  Stanislaw Osowski,et al.  Support vector machine-based expert system for reliable heartbeat recognition , 2004, IEEE Transactions on Biomedical Engineering.

[25]  June-Goo Lee,et al.  Deep Learning in Medical Imaging: General Overview , 2017, Korean journal of radiology.

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

[27]  Klaus-Robert Müller,et al.  Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.

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

[29]  J. Fleg,et al.  Cardiac Arrhythmias in a Healthy Elderly Population: Detection by 24-hour Ambulatory Electrocardiography , 1982 .

[30]  L. Køber,et al.  Excessive Supraventricular Ectopic Activity and Increased Risk of Atrial Fibrillation and Stroke , 2010, Circulation.

[31]  Kenneth S.W. Mak,et al.  The Normal Physiology of Aging , 2013 .

[32]  Junming Zhang,et al.  A New Method for Automatic Sleep Stage Classification , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[33]  Nian Cai,et al.  Image denoising method based on a deep convolution neural network , 2017, IET Image Process..

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

[35]  Yanmin Qian,et al.  Very Deep Convolutional Neural Networks for Noise Robust Speech Recognition , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

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

[37]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[38]  Xuan Zeng,et al.  HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications , 2017, IEEE Access.

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

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

[41]  Lu Cao,et al.  Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System , 2016, Sensors.

[42]  George Kurian,et al.  Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.

[43]  Joseph E. Marine,et al.  Epidemiology of arrhythmias and conduction disorders in older adults. , 2012, Clinics in geriatric medicine.

[44]  Saeed Karimifard,et al.  A robust method for diagnosis of morphological arrhythmias based on Hermitian model of higher-order statistics , 2011, Biomedical engineering online.

[45]  Chandan Chakraborty,et al.  Cardiac decision making using higher order spectra , 2013, Biomed. Signal Process. Control..

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

[47]  Yoshua Bengio,et al.  End-to-End Online Writer Identification With Recurrent Neural Network , 2017, IEEE Transactions on Human-Machine Systems.

[48]  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).