ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy Gradient

doi:10.1109/ACCESS.2019.2950383

ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy Gradient

2019-01-01

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DOI：10.1109/ACCESS.2019.2950383

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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.

Keywords:

Electrocardiography

Generative adversarial networks

Training

Generators

Gallium nitride

Mathematical model

Stochastic processes

Deep learning

generative adversarial networks

policy gradient

electrocardiogram

time series

ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy Gradient

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