Pain is defined as “a distressing experience associated with actual or potential tissue damage with sensory, emotional, cognitive and social components”, knowing the exact level of pain experienced to have a critical impact for caregivers to make diagnosis and make he suitable treatment plan, but the available methods depend entirely on the patient self-report, which increase the difficulties of knowing the accurate level of pain experienced by the patient. Therefore, automating this process became an important issue, but due to the hardness of acquiring medical data, it became difficult to build a predictive model with good performance. Generative Adversarial Networks is a framework that generates artificial data with a distribution similar to the real data, by training two networks; the generator which tries to generate new samples similar to the real ones, and the discriminator which applies a traditional supervised classification to distinguish the augmented samples, the optimal case is when the discriminator cannot distinguish the augmented samples from the real samples. In this research, we generated data using Least Square Generative Adversarial Networks and the study the effect of applying feature selection on the data before the augmentation. Moreover, the approach was tested on a dataset that contains multi biopotential signals for different levels of pain.

疼痛被定义为“与实际或潜在的组织损伤相关的令人痛苦的经历，具有感觉，情感，认知和社会成分，”他知道所经历的确切疼痛水平会对护理人员做出关键的诊断和制定合适的治疗方案产生至关重要的影响，但是可用的方法完全取决于患者的自我报告，这增加了了解患者所经历的准确疼痛程度的难度。因此，使该过程自动化成为重要的问题，但是由于获取医疗数据的难度，建立具有良好性能的预测模型变得困难。生成对抗网络是通过训练两个网络来生成分布与真实数据相似的人工数据的框架。生成器尝试生成与真实样本相似的新样本，鉴别器采用传统的监督分类来区分扩增样本，最佳情况是鉴别器无法区分扩增样本与真实样本。在这项研究中，我们使用最小二乘生成对抗网络生成数据，并研究了在增强之前对数据应用特征选择的效果。此外，该方法在包含针对不同程度疼痛的多种生物电势信号的数据集上进行了测试。我们使用最小二乘生成对抗网络生成了数据，并研究了在增强之前对数据应用特征选择的效果。此外，该方法在包含针对不同程度疼痛的多种生物电势信号的数据集上进行了测试。我们使用最小二乘生成对抗网络生成了数据，并研究了在增强之前对数据应用特征选择的效果。此外，该方法在包含针对不同程度疼痛的多种生物电势信号的数据集上进行了测试。