ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy Gradient

Electrocardiogram (ECG) is a method used by physicians to detect cardiac disease. Requirements for batch processing and accurate recognition of clinical data have led to the applications of deep-learning methods for feature extraction, classification, and denoising of ECGs; however, deep learning requires large amounts of data and multi-feature integration of datasets, with most available methods used for ECGs incapable of extracting global features or resulting in unstable, low quality training. To address these deficiencies, we proposed a novel generative adversarial architecture called RPSeqGAN using a training process reliant upon a sequence generative adversarial network (SeqGAN) algorithm that adopts the policy gradient (PG) in reinforcement learning. Based on clinical records collected from the MIT-BIH arrhythmia database, we compared our proposed model with three deep generative models to evaluate its stability by observing the variance of their loss curves. Additionally, we generated ECGs with five periods and evaluated them according to six metrics suitable for time series. The results indicate that the proposed model showed the highest stability and data quality.

[1]  Maryam Mohebbi,et al.  ECG Denoising Using Marginalized Particle Extended Kalman Filter With an Automatic Particle Weighting Strategy , 2017, IEEE Journal of Biomedical and Health Informatics.

[2]  Lantao Yu,et al.  SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient , 2016, AAAI.

[3]  Hans-Peter Kriegel,et al.  Integrating structured biological data by Kernel Maximum Mean Discrepancy , 2006, ISMB.

[4]  M. Pashna,et al.  Electrocardiogram synthesis using a Gaussian combination model (GCM) , 2007, 2007 Computers in Cardiology.

[5]  Pabitra Mitra,et al.  Generative Adversarial Learning for Reducing Manual Annotation in Semantic Segmentation on Large Scale Miscroscopy Images: Automated Vessel Segmentation in Retinal Fundus Image as Test Case , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[6]  Su Ruan,et al.  Medical Image Synthesis with Context-Aware Generative Adversarial Networks , 2016, MICCAI.

[7]  G. Clifford,et al.  Generating 24-hour ECG, BP and respiratory signals with realistic linear and nonlinear clinical characteristics using a nonlinear model , 2004, Computers in Cardiology, 2004.

[8]  Simon M. Lucas,et al.  A Survey of Monte Carlo Tree Search Methods , 2012, IEEE Transactions on Computational Intelligence and AI in Games.

[9]  Pierre-François Marteau,et al.  Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Michael I. Jordan,et al.  PEGASUS: A policy search method for large MDPs and POMDPs , 2000, UAI.

[11]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[12]  Xiong Luo,et al.  Attention-Based Relation Extraction With Bidirectional Gated Recurrent Unit and Highway Network in the Analysis of Geological Data , 2018, IEEE Access.

[13]  Xuanqin Mou,et al.  Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss , 2017, IEEE Transactions on Medical Imaging.

[14]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[15]  Victor C. M. Leung,et al.  Design and evaluation of a novel wireless three-pad ECG system for generating conventional 12-lead signals , 2010, BODYNETS.

[16]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[17]  Shamim Nemati,et al.  An artificial vector model for generating abnormal electrocardiographic rhythms. , 2010, Physiological measurement.

[18]  Tao Xu,et al.  SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation , 2017, Neuroinformatics.

[19]  Hayit Greenspan,et al.  GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification , 2018, Neurocomputing.

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

[21]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[22]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[23]  Gari D Clifford,et al.  Synthetic ECG generation and Bayesian filtering using a Gaussian wave-based dynamical model , 2010, Physiological measurement.

[24]  Quan Liu,et al.  Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network , 2019, Scientific Reports.

[25]  Behboud Mashoufi,et al.  A new personalized ECG signal classification algorithm using Block-based Neural Network and Particle Swarm Optimization , 2016, Biomed. Signal Process. Control..

[26]  Rob Fergus,et al.  Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.

[27]  Sungroh Yoon,et al.  Polyphonic Music Generation with Sequence Generative Adversarial Networks , 2017 .

[28]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

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

[30]  Georg Langs,et al.  Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.

[31]  Yann LeCun,et al.  Disentangling factors of variation in deep representation using adversarial training , 2016, NIPS.

[32]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[33]  Ferenc Huszar,et al.  How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary? , 2015, ArXiv.

[34]  Patrick E. McSharry,et al.  A dynamical model for generating synthetic electrocardiogram signals , 2003, IEEE Transactions on Biomedical Engineering.

[35]  Hashim Habiballa,et al.  ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster , 2015, TheScientificWorldJournal.

[36]  Fernando Pérez-Cruz,et al.  Kullback-Leibler divergence estimation of continuous distributions , 2008, 2008 IEEE International Symposium on Information Theory.

[37]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[38]  Yishay Mansour,et al.  Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.