Gaussian mixture model in clustering acoustic emission signals for characterizing osteoarthritic knees

Abstract

High-frequency acoustic waves produced by the sudden release of energy within a material caused by mechanical loading, deformation, corrosion, or other forms of damage are detected by Acoustic Emission (AE). It is a non-destructive testing technique utilized to monitor the structural health of materials and components and to detect and locate defects or damage that could lead to failure. Researchers in the biomedical field have lately focused on AE due to its potential for early detection, tracking, and managing various health issues, such as identifying and evaluating joint disorders, tissue damage, etc. However, due to the variance in AE signals produced from complex structures like knee joints, a general statistical method frequently finds it difficult to differentiate between distinct types of AE signals from different kinds of damage. In such instances, machine learning (ML) and deep learning (DL) can be beneficial for differentiating these signals. In this study, the application of Gaussian mixture model (GMM) clustering with principal component analysis (PCA) for dimension reduction was proposed to detect knee osteoarthritis (OA) from AE data. Four groups of participants, Group A (age 20–39), Group B (age 40–59), and Group C (age 60+), along with diagnosed OA as Group D, were included in this study. Avoiding overlapping AE signal parameters from different groups of participants is a significant challenge. GMM was applied to identify and eliminate the coinciding data points to address this issue. Moreover, the impact of this approach on clustering was also investigated in this research, and by utilizing GMM and PCA, compact clustering of the AE data potentially ensured the efficiency and effectiveness of early diagnosis of OA using the AE Technique (AET).