A Novel Method of Temporomandibular Joint Hypermobility Diagnosis Based on Signal Analysis

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), w...

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Autores principales:	Justyna Grochala, Dominik Grochala, Marcin Kajor, Joanna Iwaniec, Jolanta E. Loster, Marek Iwaniec
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hypermobile temporomandibular joint movement auscultation stethoscope signal analysis RDC/TMD Medicine R
Acceso en línea:	https://doaj.org/article/1ca812ac06c34e00aafe57ab18c6ddf0
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spelling	oai:doaj.org-article:1ca812ac06c34e00aafe57ab18c6ddf02021-11-11T17:46:01ZA Novel Method of Temporomandibular Joint Hypermobility Diagnosis Based on Signal Analysis10.3390/jcm102151452077-0383https://doaj.org/article/1ca812ac06c34e00aafe57ab18c6ddf02021-11-01T00:00:00Zhttps://www.mdpi.com/2077-0383/10/21/5145https://doaj.org/toc/2077-0383Despite 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<sup>®</sup> 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.Justyna GrochalaDominik GrochalaMarcin KajorJoanna IwaniecJolanta E. LosterMarek IwaniecMDPI AGarticlehypermobile temporomandibular joint movementauscultationstethoscopesignal analysisRDC/TMDMedicineRENJournal of Clinical Medicine, Vol 10, Iss 5145, p 5145 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hypermobile temporomandibular joint movement auscultation stethoscope signal analysis RDC/TMD Medicine R
spellingShingle	hypermobile temporomandibular joint movement auscultation stethoscope signal analysis RDC/TMD Medicine R Justyna Grochala Dominik Grochala Marcin Kajor Joanna Iwaniec Jolanta E. Loster Marek Iwaniec A Novel Method of Temporomandibular Joint Hypermobility Diagnosis Based on Signal Analysis
description	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<sup>®</sup> 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.
format	article
author	Justyna Grochala Dominik Grochala Marcin Kajor Joanna Iwaniec Jolanta E. Loster Marek Iwaniec
author_facet	Justyna Grochala Dominik Grochala Marcin Kajor Joanna Iwaniec Jolanta E. Loster Marek Iwaniec
author_sort	Justyna Grochala
title	A Novel Method of Temporomandibular Joint Hypermobility Diagnosis Based on Signal Analysis
title_short	A Novel Method of Temporomandibular Joint Hypermobility Diagnosis Based on Signal Analysis
title_full	A Novel Method of Temporomandibular Joint Hypermobility Diagnosis Based on Signal Analysis
title_fullStr	A Novel Method of Temporomandibular Joint Hypermobility Diagnosis Based on Signal Analysis
title_full_unstemmed	A Novel Method of Temporomandibular Joint Hypermobility Diagnosis Based on Signal Analysis
title_sort	novel method of temporomandibular joint hypermobility diagnosis based on signal analysis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1ca812ac06c34e00aafe57ab18c6ddf0
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A Novel Method of Temporomandibular Joint Hypermobility Diagnosis Based on Signal Analysis

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