Szczegóły publikacji
Opis bibliograficzny
A novel method of temporomandibular joint hypermobility diagnosis based on signal analysis / Justyna Grochala, Dominik GROCHALA, Marcin KAJOR, Joanna IWANIEC, Jolanta E. Loster, Marek IWANIEC // Journal of Clinical Medicine [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2077-0383. — 2021 — vol. 10 iss. 21 art. no. 5145, s. 1–13. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 12–13, Abstr. — Publikacja dostępna online od: 2021-11-02
Autorzy (6)
- Grochala Justyna
- AGHGrochala Dominik
- AGHKajor Marcin
- AGHIwaniec Joanna
- Loster Jolanta E.
- AGHIwaniec Marek
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 137435 |
|---|---|
| Data dodania do BaDAP | 2021-11-08 |
| Tekst źródłowy | URL |
| DOI | 10.3390/jcm10215145 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Clinical Medicine |
Abstract
Despite the temporomandibular joint (TMJ) being a well-known anatomical structure its diagnosis may become difficult because physiological sounds accompanying joint movement can falsely indicate pathological symptoms. One example of such a situation is temporomandibular joint hypermobility (TMJH), which still requires comprehensive study. The commonly used official research diagnostic criteria for temporomandibular disorders (RDC/TMD) does not support the recognition of TMJH. Therefore, in this paper the authors propose a novel diagnostic method of TMJH based on the digital time–frequency analysis of sounds generated by TMJ. Forty-seven volunteers were diagnosed using the RDC/TMD questionnaire and auscultated with the Littmann 3200 electronic stethoscope on both sides of the head simultaneously. Recorded TMJ sounds were transferred to the computer via Bluetooth® for numerical analysis. The representation of the signals in the time–frequency domain was computed with the use of the Python Numpy and Matplotlib libraries and short-time Fourier transform. The research reveals characteristic time–frequency features in acoustic signals which can be used to detect TMJH. It is also proved that TMJH is a rare disorder; however, its prevalence at the level of around 4% is still significant.
