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The knee joint is the largest and one of the most vulnerable and most frequently damaged joints in the human body. It is characterized by a complex structure. All articular surfaces are covered with hyaline cartilage. This cartilage has minimal regenerative capacity. Under the influence of cyclical micro-injuries, inflammatory mediators, prolonged excessive pressure or immobility, and thus disturbance of tissue nutrition, the cartilage becomes susceptible to damage and is easily covered with villi, cracks and abrasion. As a result, this translates into changes in the friction and lubrication processes within the joint and may affect the generated vibroacoustic processes. In this study, the signals recorded in a group of 28 volunteers were analysed, 15 of them were healthy people (HC) and 13 were people diagnosed with osteoarthritis (OA) qualified for surgery. The study aims to check the usefulness of the EMD (Empirical Mode Decomposition) algorithm in the filtration procedures of vibroacoustic signals. This algorithm is most often used in the analysis of signals that are most often nonlinear and non-stationary. Selected statistical indicators, such as RMS, VMS, variance and energy, were determined for the signals constituting the sum of the IMFs (Intrinsic Mode Functions) 1-8, having a normal distribution in the assessment of damage to the articular cartilage of the knee joint. Statistical analysis was performed for the values of individual indicators obtained. The vibroacoustic signals were recorded using CM-01B contact microphones placed in the central part of the medial and lateral joint fissure for movement in the range of 90°–0°–90° in closed kinetic chains (CKC) in the control group (HC) and the group of patients diagnosed with osteoarthritis (OA).

Analysis of differences in vibroacoustic signals between healthy and osteoarthritic knees using EMD algorithm and statistical analysis