The paucity of physiological time-series data collected from low-resource clinical settings limits the capabilities of modern machine learning algorithms in achieving high performance. Such performance is further hindered by class imbalance; datasets where a diagnosis is much more common than others. To overcome these two issues at low-cost while preserving privacy, data augmentation methods can be employed. In the time domain, the traditional method of time-warping could alter the underlying data distribution with detrimental consequences. This is prominent when dealing with physiological conditions that influence the frequency components of data. In this paper, we propose PlethAugment; three different conditional generative adversarial networks (CGANs) with an adapted diversity term for the generation of pathological photoplethysmogram (PPG) signals in order to boost medical classification performance. To evaluate and compare the GANs, we introduce a novel metric-agnostic method; the synthetic generalization curve . We validate this approach on two proprietary and two public datasets representing a diverse set of medical conditions. Compared to training on non-augmented class-balanced datasets, training on augmented datasets leads to an improvement of the AUROC by up to 29% when using cross validation. This illustrates the potential of the proposed CGANs to significantly improve classification performance.

中文翻译：

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PlethAugment：基于 GAN 的 PPG 增强，用于资源匮乏环境中的医疗诊断。

从资源匮乏的临床环境中收集的生理时间序列数据的缺乏限制了现代机器学习算法实现高性能的能力。阶级不平衡进一步阻碍了这种表现；诊断比其他数据更为常见的数据集。为了在保护隐私的同时以低成本克服这两个问题，可以采用数据增强方法。在时域中，传统的时间扭曲方法可能会改变底层数据分布，从而产生有害后果。在处理影响数据频率分量的生理条件时，这一点尤为突出。在本文中，我们提出PlethAugment；三个不同的条件生成对抗网络（CGAN），具有适应的多样性术语，用于生成病理光电体积描记图（PPG）信号，以提高医学分类性能。为了评估和比较 GAN，我们引入了一种新颖的与度量无关的方法; 这综合泛化曲线 。我们在代表不同医疗状况的两个专有数据集和两个公共数据集上验证了这种方法。与在非增强类平衡数据集上进行训练相比，在使用交叉验证时，在增强数据集上进行训练可使 AUROC 提高高达 29%。这说明了所提出的 CGAN 显着提高分类性能的潜力。