Aspiration is a serious complication of swallowing disorders. Adequate detection of aspiration is essential in dysphagia management and treatment. High-resolution cervical auscultation has been increasingly considered as a promising noninvasive swallowing screening tool and has inspired automatic diagnosis with advanced algorithms. The performance of such algorithms relies heavily on the amount of training data. However, the practical collection of cervical auscultation signal is an expensive and time-consuming process because of the clinical settings and trained experts needed for acquisition and interpretations. Furthermore, the relatively infrequent incidence of severe airway invasion during swallowing studies constrains the performance of machine learning models. Here, we produced supplementary training exemplars for desired class by capturing the underlying distribution of original cervical auscultation signal features using auxiliary classifier Wasserstein generative adversarial networks. A 10-fold subject cross-validation was conducted on 2079 sets of 36-dimensional signal features collected from 189 patients undergoing swallowing examinations. The proposed data augmentation outperforms basic data sampling, cost-sensitive learning and other generative models with significant enhancement. This demonstrates the remarkable potential of proposed network in improving classification performance using cervical auscultation signals and paves the way of developing accurate noninvasive swallowing evaluation in dysphagia care.

中文翻译：

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使用辅助分类器 Wasserstein 生成对抗网络改进非侵入性吸入检测

误吸是吞咽障碍的严重并发症。充分检测误吸对于吞咽困难的管理和治疗至关重要。高分辨率颈椎听诊越来越被认为是一种有前途的无创吞咽筛查工具，并激发了使用先进算法进行自动诊断的灵感。此类算法的性能在很大程度上依赖于训练数据的数量。然而，由于采集和解释需要临床环境和训练有素的专家，因此实际收集宫颈听诊信号是一个昂贵且耗时的过程。此外，在吞咽研究期间严重气道侵犯的发生率相对较低，这限制了机器学习模型的性能。这里，我们通过使用辅助分类器 Wasserstein 生成对抗网络捕获原始宫颈听诊信号特征的潜在分布，为所需类别生成了补充训练样本。对从 189 名接受吞咽检查的患者收集的 2079 组 36 维信号特征进行了 10 倍受试者交叉验证。所提出的数据增强优于基本数据采样、成本敏感学习和其他具有显着增强的生成模型。这证明了所提出的网络在使用颈部听诊信号提高分类性能方面的显着潜力，并为在吞咽困难护理中开发准确的无创吞咽评估铺平了道路。