We present a new method for quantifying signal quality of joint acoustic emissions (JAEs) from the knee during unloaded flexion/extension (F/E) exercises. For ten F/E cycles, JAEs were recorded, in a clinical setting, from 34 healthy knees and 13 with a meniscus tear (n = 24 subjects). The recordings were first segmented by F/E cycle and described using time and frequency domain features. Using these features, a symmetric k-nearest neighbor graph was created and described using a spectral embedding. We show how the underlying community structure of JAEs was comparable across joint health levels and was highly affected by artifacts. Each F/E cycle was scored by its distance from a diverse set of manually annotated, clean templates and removed if above the artifact threshold. We validate this methodology by showing an improvement in the distinction between the JAEs of healthy and injured knees. Graph community factor (GCF) was used to detect the number of communities in each recording and describe the heterogeneity of JAEs from each knee. Before artifact removal, there was no significant difference between the healthy and injured groups due to the impact of artifacts on the community construction. Following implementation of artifact removal, we observed improvement in knee health classification. The GCF value for the meniscus tear group was significantly higher than the healthy group (p < 0.01). With more JAE recordings being taken in the clinic and at home, this paper addresses the need for a robust artifact removal method which is necessary for an accurate description of joint health.

中文翻译：

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使用基于图形的频谱嵌入量化联合声发射的信号质量

我们提出了一种新方法，用于在无负荷屈伸 (F/E) 练习期间量化来自膝关节的关节声发射 (JAE) 的信号质量。对于 10 个 F/E 周期，在临床环境中记录了 34 个健康膝关节和 13 个半月板撕裂（n = 24 名受试者）的 JAE。录音首先按 F/E 循环分段，并使用时域和频域特征进行描述。使用这些特征，使用谱嵌入创建和描述了一个对称的 k 最近邻图。我们展示了 JAE 的潜在社区结构如何在关节健康水平上具有可比性，并且受到人工制品的高度影响。每个 F/E 循环根据其与一组不同的手动注释、干净模板的距离进行评分，如果高于伪影阈值，则将其删除。我们通过展示健康和受伤膝盖的 JAE 之间的区别来验证这种方法。图社区因子 (GCF) 用于检测每个记录中的社区数量，并描述每个膝关节 JAE 的异质性。在文物移除前，由于文物对社区建设的影响，健康组和受伤组之间没有显着差异。在执行伪影清除后，我们观察到膝关节健康分类有所改善。半月板撕裂组的 GCF 值显着高于健康组（p < 0.01）。随着越来越多的 JAE 记录在诊所和家中进行，本文解决了对准确描述关节健康所必需的强大伪影去除方法的需求。