Research Area: Machine Learning
In this paper, we propose a synthetic generation method for time-series data based on generative adversarial networks (GANs) and apply it to data augmentation for biosignal 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.
Keywords:
Biosignal
Data Augmentation
Generative Adversarial Networks
Machine Learning
Deep Learning
Author(s) Name: Shota Haradal; Hideaki Hayashi; Seiichi Uchida
Journal name:
Conferrence name: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher name: IEEE
DOI: 10.1109/EMBC.2018.8512396
Volume Information:
