Quantifying Signal Quality for Joint Acoustic Emissions Using Graph-Based Spectral Embedding.
Quantifying Signal Quality for Joint Acoustic Emissions Using Graph-Based Spectral Embedding.
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DOI：
10.1109/jsen.2021.3071664
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发表时间：
2021-06-15
影响因子：
4.3
通讯作者：
Inan OT
中科院分区：
文献类型：
--
作者：
Richardson KL;Gharehbaghi S;Ozmen GC;Safaei MM;Inan OT
提出了一种新的量化膝关节无负荷屈伸(F/E)训练时关节声发射信号质量的方法。在10个F/E周期中，在临床环境下，记录了34个健康膝关节和13个半月板撕裂患者(n=24)的JAE。这些记录首先被F/E周期分割，并使用时间域和频域特征来描述。利用这些特征，创建了对称k近邻图，并使用谱嵌入来描述该图。我们展示了JAE的底层社区结构如何在联合健康水平上具有可比性，并受到人工制品的高度影响。每个F/E周期根据其与一组不同的手动注释的干净模板的距离进行评分，如果超过伪影阈值，则将其移除。我们通过显示健康和受伤膝盖的JAE之间的区别的改善来验证这一方法。使用图形群落因子(GCF)来检测每个记录中的群落数量，并描述来自每个膝关节的JAE的异质性。在人工制品去除之前，由于人工制品对社区建设的影响，健康组和受伤组之间没有显著差异。在实施伪影去除后，我们观察到膝关节健康分类的改善。半月板撕裂组的GCF值显著高于健康组(P<；0.01)。随着越来越多的JAE记录在临床和家庭中被采用，本文解决了对健壮的伪影去除方法的需求，这对于准确描述关节健康是必要的。
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.
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DOI：
10.1109/jbhi.2014.2338351
发表时间：
2015-05-01
影响因子：
7.7
作者：
Orphanidou, Christina;Bonnici, Timothy;Tarassenko, Lionel
通讯作者：
Tarassenko, Lionel
影响因子：
3.9
作者：
Bolus, Nicholas B.;Jeong, Hyeon Ki;Inan, Omer T.
通讯作者：
Inan, Omer T.
影响因子：
4.6
作者：
Behar, Joachim;Oster, Julien;Clifford, Gari D.
通讯作者：
Clifford, Gari D.
影响因子：
4.4
作者：
Aichner, R;Buchner, H;Kellermann, W
通讯作者：
Kellermann, W
DOI：
10.1155/s1110865703305050
发表时间：
2003-10-01
期刊：
EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING
影响因子：
--
作者：
Cohen, I;Gannot, S;Berdugo, B
通讯作者：
Berdugo, B