Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier

The interpretation of Myocardial Infarction (MI) via electrocardiogram (ECG) signal is a challenging task. ECG signals’ morphological view show significant variation in different patients under different physical conditions. Several learning algorithms have been studied to interpret MI. However, the drawback of machine learning is the use of heuristic features with shallow feature learning architectures. To overcome this problem, a deep learning approach is used for learning features automatically, without conventional handcrafted features. This paper presents sequence modeling based on deep learning with recurrent network for ECG-rhythm signal classification. The recurrent network architecture such as a Recurrent Neural Network (RNN) is proposed to automatically interpret MI via ECG signal. The performance of the proposed method is compared to the other recurrent network classifiers such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The objective is to obtain the best sequence model for ECG signal processing. This paper also aims to study a proper data partitioning ratio for the training and testing sets of imbalanced data. The large imbalanced data are obtained from MI and healthy control of PhysioNet: The PTB Diagnostic ECG Database 15-lead ECG signals. According to the comparison result, the LSTM architecture shows better performance than standard RNN and GRU architecture with identical hyper-parameters. The LSTM architecture also shows better classification compared to standard recurrent networks and GRU with sensitivity, specificity, precision, F1-score, BACC, and MCC is 98.49%, 97.97%, 95.67%, 96.32%, 97.56%, and 95.32%, respectively. Apparently, deep learning with the LSTM technique is a potential method for classifying sequential data that implements time steps in the ECG signal.

[1]  Mark E Josephson,et al.  Use of the electrocardiogram in acute myocardial infarction. , 2003, The New England journal of medicine.

[2]  S. Steinhubl,et al.  Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial , 2018, JAMA.

[3]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals , 2018, Applied Intelligence.

[4]  Yufei Tao,et al.  Research and Realization of ECG Classification based on Gated Recurrent Unit , 2018, 2018 Chinese Automation Congress (CAC).

[5]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

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

[7]  James Samuel Fleming.  Interpreting the Electrocardiogram , 1980 .

[8]  Wahyu Caesarendra,et al.  Suddent cardiac death predictor based on spatial QRS-T angle feature and support vector machine case study for cardiac disease detection in Indonesia , 2016, 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[9]  A. Goldberger.  Clinical Electrocardiography: A Simplified Approach , 1977 .

[10]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[11]  Jeroen J. Bax,et al.  Universal definition of myocardial infarction. , 2007 .

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

[13]  Stefan C. Kremer,et al.  Recurrent Neural Networks , 2013, Handbook on Neural Information Processing.

[14]  Akshay Khandelwal,et al.  The Comparison of Physician to Computer Interpreted Electrocardiograms on ST-elevation Myocardial Infarction Door-to-balloon Times. , 2016, Critical pathways in cardiology.

[15]  Nils Strodthoff,et al.  Detecting and interpreting myocardial infarction using fully convolutional neural networks , 2018, Physiological measurement.

[16]  Annisa Darmawahyuni,et al.  Coronary Heart Disease Interpretation Based on Deep Neural Network , 2019, Computer Engineering and Applications Journal.

[17]  Vladlen Koltun,et al.  An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling , 2018, ArXiv.

[18]  S. Nurmaini,et al.  Cardiac Arrhythmias Classification Using Deep Neural Networks and Principle Component Analysis Algorithm , 2018 .

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

[20]  Geoffrey E. Hinton,et al.  A Simple Way to Initialize Recurrent Networks of Rectified Linear Units , 2015, ArXiv.

[21]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[22]  Sabri Boughorbel,et al.  Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric , 2017, PloS one.

[23]  Hin Wai Lui,et al.  Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices , 2018 .

[24]  Shraddha Singh,et al.  Classification of ECG Arrhythmia using Recurrent Neural Networks , 2018 .

[25]  Jordi Torres,et al.  Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks , 2017, ICLR.

[26]  Yoshua Bengio,et al.  Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations , 2016, ICLR.

[27]  Madhuchhanda Mitra,et al.  Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification , 2014, IEEE Transactions on Instrumentation and Measurement.

[28]  Thomas Wiatowski,et al.  A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction , 2015, IEEE Transactions on Information Theory.

[29]  Keiichi Fukuda,et al.  Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients , 2019, PloS one.

[30]  M. I. Gabriel Khan,et al.  Rapid ECG Interpretation , 1997 .

[31]  L. Gallo.  Cardiovascular Disease , 1995, GWUMC Department of Biochemistry Annual Spring Symposia.