Background and Objective: Respiratory gating training is a common technique to increase patient proprioception, with the goal of (e.g.) minimizing the effects of organ motion during radiotherapy. In this work, we devise a system based on autoencoders for classification of regular, apnea and unconstrained breathing patterns (i.e. multiclass). Methods: Our approach is based on morphological analysis of the respiratory signals, using an autoencoder trained on regular breathing. The correlation between the input and output of the autoencoder is used to train and test several classifiers in order to select the best. Our approach is evaluated in a novel real-world respiratory gating biofeedback training dataset and on the Apnea-ECG reference dataset. Results: Accuracies of 95 ± 3.5% and 87 ± 6.6% were obtained for two different datasets, in the classification of breathing and apnea. These results suggest the viability of a generalised model to characterise the breathing patterns under study. Conclusions: Using autoencoders to learn respiratory gating training patterns allows a data-driven approach to feature extraction, by focusing only on the signal’s morphology. The proposed system is prone to be used in real-time and could potentially be transferred to other domains.

背景与目的：呼吸门控训练是增加患者本体感受的常用技术，其目的是（例如）使放射治疗期间器官运动的影响最小化。在这项工作中，我们设计了一种基于自动编码器的系统，用于对常规，呼吸暂停和无约束呼吸模式（即多类）进行分类。方法：我们的方法是基于呼吸信号的形态分析，并使用经过定期呼吸训练的自动编码器。自动编码器的输入和输出之间的相关性用于训练和测试多个分类器，以选择最佳分类器。我们的方法在一个新颖的现实世界中的呼吸门控生物反馈训练数据集和呼吸暂停-ECG参考数据集中进行了评估。结果：两个不同的数据集的准确度分别为95±3.5％和87±6.6％，在呼吸和呼吸暂停的分类中。这些结果表明，通用模型能够表征正在研究的呼吸模式。结论：使用自动编码器来学习呼吸门控训练模式，可以通过仅关注信号的形态来采用数据驱动的特征提取方法。所建议的系统易于实时使用，并且有可能被转移到其他域。