Analysis of knee joint acoustic emissions using variational mode decomposition and sample entropy for osteoarthritis detection
Artykuł w czasopiśmie
MNiSW
140
Lista 2024
| Status: | |
| Autorzy: | Karpiński Robert, Syta Arkadiusz |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 118 |
| Numer artykułu: | 109791 |
| Strony: | 1 - 15 |
| Impact Factor: | 4,9 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The detection of osteoarthritis (OA) remains a major challenge in musculoskeletal diagnostics, largely due to the limited sensitivity of conventional imaging techniques in identifying cartilage degeneration. This study explores the potential of vibroarthrography (VAG) combined with Variational Mode Decomposition (VMD) and Sample Entropy (SE) as a noninvasive method for differentiating healthy and osteoarthritic knee joints. Acoustic signals were recorded using three contact microphones during flexion–extension movements performed under two biomechanical conditions: open (OKC) and closed kinetic chain (CKC). VMD was applied to decompose the signals into intrinsic mode functions (IMFs), enabling frequency-specific analysis, while SE was calculated for each IMF to quantify signal complexity and regularity. Statistically significant differences (p < 0.01) in IMF frequency characteristics were observed between OA and control groups, particularly under CKC conditions, highlighting the sensitivity of joint loading to biomechanical alterations. Lower SE values in OA patients indicated reduced neuromuscular adaptability and increased signal regularity within specific frequency bands. Among all sensors, MIC2 demonstrated the highest discriminative potential. An exploratory classification analysis using logistic regression further supported these findings by yielding higher separability under CKC than OKC. These findings suggest that CKC-based VAG analysis using VMD and SE provides valuable diagnostic insights into knee joint biomechanics and may facilitate OA detection. Overall, the results underscore the potential of VAG as a low-cost, noninvasive, and dynamic diagnostic tool complementing traditional imaging modalities. |
