Haradal Shota, Hayashi Hideaki, Uchida Seiichi
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:368-371. doi: 10.1109/EMBC.2018.8512396.
In this paper, we propose a synthetic generationmethod for time-series data based on generative adversarial networks (GANs) and apply it to data augmentation for biosinal classification. GANs are a recently proposed framework for learning a generative model, where two neural networks, one generating synthetic data and the other discriminating synthetic and real data, are trained while competing with each other. In the proposed method, each neural network in GANs is developed based on a recurrent neural network using long short-term memories, thereby allowing the adaptation of the GANs framework to time-series data generation. In the experiments, we confirmed the capability of the proposed method for generating synthetic biosignals using the electrocardiogram and electroencephalogram datasets. We also showed the effectiveness of the proposed method for data augmentation in the biosignal classification problem.
在本文中，我们提出了一种基于生成对抗网络（GAN）的时间序列数据合成生成方法，并将其应用于生物信号分类的数据增强。GAN是最近提出的一种用于学习生成模型的框架，其中两个神经网络，一个生成合成数据，另一个区分合成数据和真实数据，在相互竞争的同时进行训练。在所提出的方法中，GAN中的每个神经网络都是基于使用长短期记忆的循环神经网络开发的，从而使GAN框架能够适应时间序列数据生成。在实验中，我们使用心电图和脑电图数据集证实了所提出方法生成合成生物信号的能力。我们还展示了所提出方法在生物信号分类问题中进行数据增强的有效性。
