Application of nonlinear properties of acoustic signals for diagnosing knee joint damage
Artykuł w czasopiśmie
MNiSW
140
Lista 2024
| Status: | |
| Autorzy: | Karpiński Robert, Syta Arkadiusz, Machrowska Anna, Krakowski Przemysław, Maciejewski Marcin, Jonak Józef |
| Dyscypliny: | |
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| Rok wydania: | 2026 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Wolumen/Tom: | 116 |
| Numer artykułu: | 109497 |
| Strony: | 1 - 14 |
| Impact Factor: | 4,9 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| This study proposes a non-invasive method for detecting knee joint cartilage damage based on acoustic signals recorded during two different test procedures (open and closed kinematic chains). The method aims to assist medical personnel in the diagnostic process while maintaining broad accessibility and low examination costs. We developed a custom measurement system with acoustic sensors placed at anatomical locations of the knee joint. The recorded signals exhibit nonlinear characteristics resulting from joint structure complexity, frictional in- teractions, and dynamic loading during motion. Due to the complexity of the joint under study, we selected nonlinear features extracted using recurrence indicators to describe its state. The applied machine learning methods allowed us to estimate both the location and the informational load of each sensor. Notably, combining all signal sources enabled binary classification accuracy of up to 92%. These findings suggest that the proposed method provides an effective, low-cost, and clinically applicable approach for the assessment of cartilage degeneration. Although the study included patients with clinically confirmed osteoarthritis, the results indicate the potential of nonlinear vibroacoustic analysis to support future research on earlier detection and monitoring of degenerative changes |
