An Interpretable Experimental Data Augmentation Method to Improve Knee Health Classification Using Joint Acoustic Emissions.
An Interpretable Experimental Data Augmentation Method to Improve Knee Health Classification Using Joint Acoustic Emissions.
复制标题
一种利用关节声发射改善膝关节健康分类的可解释实验数据增强方法。
DOI：
10.1007/s10439-021-02788-x
复制
发表时间：
2021
影响因子：
3.8
通讯作者：
Inan,OmerT
中科院分区：
文献类型：
--
作者：
Ozmen,GoktugC;Gazi,AsimH;Gharehbaghi,Sevda;Richardson,KristineL;Safaei,Mohsen;Whittingslow,DanielC;Prahalad,Sampath;Hunnicutt,JenniferL;Xerogeanes,JohnW;Snow,TeresaK;Inan,OmerT
从膝关节测量的关节声发射（JAE）的特征已被证明包含有关基础关节健康的信息。研究人员已经开发出处理JAE测量结果的方法，并将其与机器学习算法相结合，用于膝关节损伤诊断。虽然这些方法是基于在受控环境中测量的JAE，但我们预计JAE测量可以在现场部署环境中实现对急性膝关节损伤的可访问和可负担的诊断。然而，在这种环境中，噪声和干扰将大于无菌实验室环境，这可能会降低使用JAE的现有膝关节健康分类方法的性能。为了解决JAE测量的客观噪声和干扰检测方法的需要，作为现场部署设置的一步，我们提出了一种新的实验数据增强方法来定位，然后删除在临床环境中测量的JAE损坏的部分。在诊所中，我们招募了30名参与者，并收集了双侧膝关节的数据，总计60个膝关节（36个健康膝关节和24个受伤膝关节），随后用于膝关节健康分类。我们还招募了10名健康参与者来收集伪影和关节音（JS）点击模板，这些模板是来自膝关节的可听、短持续时间和高振幅JAE。提取光谱和时间特征，通过将现有的临床数据集融合到实验收集的模板中，在五维子空间中增强临床数据。然后，通过训练和测试线性软分类器，利用留一主题交叉验证（LOSO-CV）计算膝关节评分。使用逻辑回归分类器（灵敏度= 0.75，特异性= 0.78），基线性能的曲线下面积（AUC）为0.76，无任何窗口去除。我们使用所提出的算法获得了0.86的AUC（灵敏度= 0.80，特异性= 0.89），平均而言，所有临床数据的95%用于实现此性能。该算法通过识别和收集JAE测量中常见的伪影源来增加信息，从而提高了膝关节健康分类性能。当与可穿戴系统相结合时，这种方法可以为服务不足的人群和需要在现场部署环境中进行损伤点诊断的个人提供临床相关的补充信息。
The characteristics of joint acoustic emissions (JAEs) measured from the knee have been shown to contain information regarding underlying joint health. Researchers have developed methods to process JAE measurements and combined them with machine learning algorithms for knee injury diagnosis. While these methods are based on JAEs measured in controlled settings, we anticipate that JAE measurements could enable accessible and affordable diagnosis of acute knee injuries also in field-deployable settings. However, in such settings, the noise and interference would be greater than in sterile, laboratory environments, which could decrease the performance of existing knee health classification methods using JAEs. To address the need for an objective noise and interference detection method for JAE measurements as a step towards field-deployable settings, we propose a novel experimental data augmentation method to locate and then, remove the corrupted parts of JAEs measured in clinical settings. In the clinic, we recruited 30 participants, and collected data from both knees, totaling 60 knees (36 healthy and 24 injured knees) to be used subsequently for knee health classification. We also recruited 10 healthy participants to collect artifact and joint sounds (JS) click templates, which are audible, short duration and high amplitude JAEs from the knee. Spectral and temporal features were extracted, and clinical data was augmented in five-dimensional subspace by fusing the existing clinical dataset into experimentally collected templates. Then knee scores were calculated by training and testing a linear soft classifier utilizing leave-one-subject-out cross-validation (LOSO-CV). The area under the curve (AUC) was 0.76 for baseline performance without any window removal with a logistic regression classifier (sensitivity = 0.75, specificity = 0.78). We obtained an AUC of 0.86 with the proposed algorithm (sensitivity = 0.80, specificity = 0.89), and on average, 95% of all clinical data was used to achieve this performance. The proposed algorithm improved knee health classification performance by the added information through identification and collection of common artifact sources in JAE measurements. This method when combined with wearable systems could provide clinically relevant supplementary information for both underserved populations and individuals requiring point-of-injury diagnosis in field-deployable settings.