软高斯混合模型聚类对骨关节炎膝关节声发射特征的鲁棒性分析

IF 2.3 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Nazmush Sakib , Tawhidul Islam Khan , Md. Mehedi Hassan , Shuya Ide
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引用次数: 0

摘要

声发射(AE)是一种完善的非破坏性评估(NDE)方法,目前在早期检测膝关节骨关节炎(OA)方面具有巨大的潜力。膝关节具有固有的复杂性,导致获得的声发射信号具有明显的可变性。这个问题使得区分不同膝关节状况的声发射特征变得复杂。在这方面,机器学习(ML)算法,特别是高斯混合模型(GMM),可以通过识别不同膝关节状况产生的重叠数据点来解决这一问题。由于数据集较小,早期研究在其发现的普遍性方面存在局限性。因此,需要对软GMM聚类处理重叠数据点的鲁棒性进行综合评价。目前的研究通过研究GMM聚类在检测膝关节重叠声发射数据方面的鲁棒性,进一步弥合了这一知识差距。本研究对软GMM聚类前后的聚类属性进行了全面的统计分析,以识别和去除重叠数据点。本研究的结果证实了软GMM在聚类声发射特征以智能评估膝关节健康方面的稳健性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robustness analysis of soft Gaussian Mixture Model clustering for acoustic emission features in characterizing osteoarthritic knees
Acoustic emission (AE) is a well-established non-destructive evaluation (NDE) method that currently holds enormous potential for the early detection of knee osteoarthritis (OA). Knee joints have intrinsic complexity, resulting in marked variability of the obtained AE signals. This problem complicates the distinction between the AE signatures of different knee conditions. In this regard, Machine learning (ML) algorithms, particularly the Gaussian Mixture Model (GMM), can solve this problem by identifying the overlapping data points generated from different knee joint conditions. Early studies had limitations in the generalizability of their findings due to the small dataset. Therefore, a comprehensive evaluation of the robustness of soft GMM clustering in handling overlapping data points is needed. The current study constitutes further efforts to bridge this knowledge gap by investigating the robustness of GMM clustering in detecting overlapping AE data from knee joints. This study presents a comprehensive statistical analysis of cluster properties before and after soft GMM clustering to identify and remove overlapping data points. The results of this investigation confirm the robustness of soft GMM in clustering AE features for the intelligent assessment of knee health.
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来源期刊
Medical Engineering & Physics
Medical Engineering & Physics 工程技术-工程:生物医学
CiteScore
4.30
自引率
4.50%
发文量
172
审稿时长
3.0 months
期刊介绍: Medical Engineering & Physics provides a forum for the publication of the latest developments in biomedical engineering, and reflects the essential multidisciplinary nature of the subject. The journal publishes in-depth critical reviews, scientific papers and technical notes. Our focus encompasses the application of the basic principles of physics and engineering to the development of medical devices and technology, with the ultimate aim of producing improvements in the quality of health care.Topics covered include biomechanics, biomaterials, mechanobiology, rehabilitation engineering, biomedical signal processing and medical device development. Medical Engineering & Physics aims to keep both engineers and clinicians abreast of the latest applications of technology to health care.
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