School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
Research Center for Intelligent Science and Engineering Technology of TCM, China Academy of Chinese Medical Sciences, Beijing 100000, China.
Sensors (Basel). 2023 Jan 28;23(3):1450. doi: 10.3390/s23031450.
For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this study, for the first time, we address the challenging by presenting a novel one-dimension generative adversarial networks (GAN) for generating wrist pulse signals, which manages to learn a mapping strategy from a random noise space to the original wrist pulse data distribution automatically. Concretely, Wasserstein GAN with gradient penalty (WGAN-GP) is employed to alleviate the mode collapse problem of vanilla GANs, which could be able to further enhance the performance of the generated pulse data. We compared our proposed model performance with several typical GAN models, including vanilla GAN, deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN). To verify the feasibility of the proposed algorithm, we trained our model with a dataset of real recorded wrist pulse signals. In conducted experiments, qualitative visual inspection and several quantitative metrics, such as maximum mean deviation (MMD), sliced Wasserstein distance (SWD) and percent root mean square difference (PRD), are examined to measure performance comprehensively. Overall, WGAN-GP achieves the best performance and quantitative results show that the above three metrics can be as low as 0.2325, 0.0112 and 5.8748, respectively. The positive results support that generating wrist pulse data from a small ground truth is possible. Consequently, our proposed WGAN-GP model offers a potential innovative solution to address data scarcity challenge for researchers working with computational pulse diagnosis, which are expected to improve the performance of pulse diagnosis algorithms in the future.
在过去的几年中，深度学习方法在计算脉象诊断中的应用受到了越来越多的关注。然而，由于隐私风险或实验成本高昂，其发展受到一个因素的制约，即腕部脉象数据较小。在这项研究中，我们首次提出了一种新颖的一维生成对抗网络（GAN）来生成腕部脉象信号，该方法能够自动学习从随机噪声空间到原始腕部脉象数据分布的映射策略。具体来说，我们使用带有梯度惩罚的 Wasserstein GAN（WGAN-GP）来缓解常规 GAN 中的模式崩溃问题，从而进一步提高生成脉象数据的性能。我们将所提出的模型性能与几种典型的 GAN 模型进行了比较，包括常规 GAN、深度卷积 GAN（DCGAN）和 Wasserstein GAN（WGAN）。为了验证所提出算法的可行性，我们使用真实记录的腕部脉象信号数据集对模型进行了训练。在进行的实验中，我们通过定性视觉检查和几个定量指标，如最大均值偏差（MMD）、切片 Wasserstein 距离（SWD）和均方根差百分比（PRD），来全面衡量性能。总体而言，WGAN-GP 取得了最佳的性能，定量结果表明，这三个指标分别可以低至 0.2325、0.0112 和 5.8748。阳性结果表明，从较小的真实数据中生成腕部脉象数据是可行的。因此，我们提出的 WGAN-GP 模型为解决计算脉象诊断中数据稀缺的挑战提供了一种潜在的创新解决方案，有望提高未来脉象诊断算法的性能。
