Objective: This work proposes a semi-supervised training approach for detecting lung and heart sounds simultaneously with only one trained model and in invariance to the auscultation point. Methods: We use open-access data from the 2016 Physionet/CinC Challenge, the 2022 George Moody Challenge, and from the lung sound database HF_V1. We first train specialist single-task models using foreground ground truth (GT) labels from different auscultation databases to identify background sound events in the respective lung and heart auscultation databases. The pseudo-labels generated in this way were combined with the ground truth labels in a new training iteration, such that a new model was subsequently trained to detect foreground and background signals. Benchmark tests ensured that the newly trained model could detect both, lung, and heart sound events in different auscultation sites without regressing on the original task. We also established hand-validated labels for the respective background signal in heart and lung sound auscultations to evaluate the models. Results: In this work, we report for the first time results for i) a multi-class prediction for lung sound events and ii) for simultaneous detection of heart and lung sound events and achieve competitive results using only one model. The combined multi-task model regressed slightly in heart sound detection and gained significantly in lung sound detection accuracy with an overall macro F1 score of 39.2% over six classes, representing a 6.7% improvement over the single-task baseline models. Conclusion/Significance: To the best of our knowledge, this is the first approach developed to date for measuring heart and lung sound events invariant to both, the auscultation site and capturing device. Hence, our model is capable of performing lung and heart sound detection from any auscultation location.
翻译:这项工作提出了一种半监督的培训方法,用于检测肺和心脏的听觉,同时使用一个经过训练的模型,并同时使用一个不起作用的模型。方法:我们使用2016年Physionet/CinC挑战、2022George Mody挑战、以及肺声数据库HF_V1.我们首先用不同的地面地面前方真相标签来培训专家单项任务模型,以确定肺和心脏培养数据库中的背景声音事件。以这种方式生成的假标签与新的培训周期中的地面真相标签相结合,因此,我们随后对一个新的模型进行了检测地面和背景信号的培训。基准测试确保了新培训的模型可以在不倒退原任务的情况下在不同的封闭地点探测肺部和心脏声音事件。我们还为心脏和心脏培养数据库中各自的背景信号建立了手效模型标签。结果:在这项工作中,我们第一次在心脏诊断和心脏进步方法中,第一次测量了一种稳定的心脏诊断结果,然后在一次心脏检测中,然后在一次心脏检测中,一个心脏检测和一次测试的概率记录结果。