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

Abstract

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).

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part I: Femoral-Tibial Joint

“…A summary of the parameters determined for the discrete signals used in this paper is presented below. These measures were selected based on literature data and previous studies of the authors selection of optimal signal features [ 28 , 29 ].…”

“…Such diagnostic tools should be reproducible, cheap, easy in evaluation and accessible. Vibroarthrography (VAG) seems to fulfill these requirements [ 28 , 29 ]. VAG is a measurement of vibrations or sounds generated during the movement of the joint, while classical methods of diagnostic imaging only provide information from a static position.…”

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part II: Patellofemoral Joint

“…The number of signal indices (parameters) used in vibroacoustic diagnostics is very large. The selection of the signal measures used in this paper was based on a literature analysis [ 90 , 91 , 92 , 93 , 94 ] and the previous experience of the authors presented in papers [ 51 , 52 , 95 ].…”

“…A detailed description of the indicators is presented in Part I of this paper and earlier papers by the authors [ 51 , 52 , 95 , 96 ].…”

“…In 1902, Blodgett [ 45 ] published his paper on the correlation between the sounds generated by the knee joint on auscultation and OA. Throughout the 20th and 21st centuries, numerous groups of researchers have developed this method of assessing articular cartilage from both acoustic [ 46 , 47 , 48 , 49 , 50 , 51 , 52 ] and vibrational signals [ 53 , 54 , 55 , 56 , 57 , 58 ]. Vibroarthrography (VAG), which is a measurement of the vibrations or sounds generated during joint movement, has achieved >90% accuracy in detection OA in the knee joint [ 59 ].…”