TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data
Augmentation
- URL: http://arxiv.org/abs/2206.13676v1
- Date: Tue, 28 Jun 2022 01:01:34 GMT
- Title: TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data
Augmentation
- Authors: Xiaomin Li, Anne Hee Hiong Ngu, Vangelis Metsis
- Abstract summary: We present TTS-CGAN, a conditional GAN model that can be trained on existing multi-class datasets and generate class-specific synthetic time-series sequences.
Synthetic sequences generated by our model are indistinguishable from real ones, and can be used to complement or replace real signals of the same type.
- Score: 5.607676459156789
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Signal measurement appearing in the form of time series is one of the most
common types of data used in medical machine learning applications. Such
datasets are often small in size, expensive to collect and annotate, and might
involve privacy issues, which hinders our ability to train large,
state-of-the-art deep learning models for biomedical applications. For
time-series data, the suite of data augmentation strategies we can use to
expand the size of the dataset is limited by the need to maintain the basic
properties of the signal. Generative Adversarial Networks (GANs) can be
utilized as another data augmentation tool. In this paper, we present TTS-CGAN,
a transformer-based conditional GAN model that can be trained on existing
multi-class datasets and generate class-specific synthetic time-series
sequences of arbitrary length. We elaborate on the model architecture and
design strategies. Synthetic sequences generated by our model are
indistinguishable from real ones, and can be used to complement or replace real
signals of the same type, thus achieving the goal of data augmentation. To
evaluate the quality of the generated data, we modify the wavelet coherence
metric to be able to compare the similarity between two sets of signals, and
also conduct a case study where a mix of synthetic and real data are used to
train a deep learning model for sequence classification. Together with other
visualization techniques and qualitative evaluation approaches, we demonstrate
that TTS-CGAN generated synthetic data are similar to real data, and that our
model performs better than the other state-of-the-art GAN models built for
time-series data generation.
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