Search

Differential diagnosis depends in cases with disk displacement on accurate identification of sound source. Mistakes may occur when clicking from one temporomandibular joint (TMJ) is heard on both sides of the head at auscultation and neither examiner nor patient, is sure about side. The hypothesis was that the head tissues affect spectral characteristics of TMJ sounds and that differences due to different positioning of sensors can be used in localization of source. The aim was to compare bilateral electronic recordings of unilateral TMJ sounds to obtain and compare attenuation, phase shift and time delay. Recordings were made from 12 subjects with unilateral clicking. Small electret condenser microphones, bandwidth 40-20 000 Hz, were placed at the openings of the auditory canals and the sounds were recorded at a sampling rate of 48 000 Hz. The head tissues acted as a filter causing a frequency dependent attenuation and phase shift. There was a time difference between the ipsi- and the contra lateral recordings, the latter always having a longer delay time (range 0.2-1.2 ms, group mean 0.68 ms, s.d. 0.292 ms). In conclusion, spectral analysis of bilateral electronic TMJ sound recordings is of diagnostic value when bilateral clicking is heard at auscultation and can help to avoid diagnosing a silent joint as clicking.

Sounds, such as clicking and/or crepitation, evoked in the temporomandibular (jaw) joint during function may indicate pathology. Analysis of the reduced interference time-frequency distribution of these sounds is of diagnostic value. However, visual evaluation is expensive and error prone, and there is, thus, a need for automated analysis. The aim of this study was to find the optimal signal representation and pattern recognition method for computerized classification of temporomandibular joint sounds. Concepts of time-shift invariance with and without scale invariance were employed and mutually compared. The automated analysis methods provided classification results that were similar to previous visual classification of the sounds. It was found that the classifier performance was significantly improved when scale invariance was omitted. This behavior occurred because scale invariance interfered with the frequency content of the signal. Therefore, scale invariance should not be pursued in the classification scheme employed in this study.

Temporomandibular Joint (TMJ) sounds, clicking and crepitation, are important signs of possible TM disorder or dysfunction (TMD). The sound are usually recorded and observed by stethoscope auscultation or palpation. Sound from one TMJ may propagate through head tissues and be recorded on the contra lateral side misleading the examiner to classify both joints as non-silent. Errors in localization of sound source may lead to an erroneous diagnosis. Widmalm et al. (1997) suggested a mathematical model for estimation of the sound propagation characteristics through the head tissues. A modified model applying the auto-spectral density and cross-spectral density of the signal was used to estimate the bilateral sound propagation characteristics of temporomandibular joint sounds from two subjects. The result indicates that the head tissues act as a bandpass filter causing strong attenuation in some frequency areas with little attenuation in others. The phase response of the transfer function provides a good mean to estimate the latency in time between sounds.

Sounds evoked in the temporomandibular joint (TMJ) during jaw movements may indicate pathology. They are in dental clinics usually recorded by auscultation and noted in protocols by verbal, subjective descriptions. Time-frequency analysis of electronically recorded TMJ sounds makes possible a more objective and sophisticated analysis. Such sounds were recorded from four subjects and grouped into two sets. One was used for training a classifier, while the other was used for testing its ability to relate a given sound to the subject from which it was recorded. Both scale and time-shift invariant representations, as well as only time-shift invariant representations of the Reduced Interference Distributions of the TMJ sounds were used for pattern recognition. The nearest neighbor, zero-subspace and nearest constrained linear combination classification methods were employed. It was observed that TMJ sound patterns could be very typical for a given person. This indicates that our classification approach can be developed into a useful diagnostic tool by obtaining training sets from patients where a definitive diagnosis of TMJ pathology has been obtained.

summary There are conflicting opinions about the frequency range of temporomandibular joint (TMJ) sounds. Some authors claim that the upper limit is about 650 Hz. The aim was to test the hypothesis that TMJ sounds may contain frequencies well above 650 Hz but that significant amounts of their energy are lost if the vibrations are recorded using contact sensors and/or travel far through the head tissues. Time–frequency distributions of 172 TMJ clickings (three subjects) were compared between recordings with one microphone in the ear canal and a skin contact transducer above the clicking joint and between recordings from two microphones, one in each ear canal. The energy peaks of the clickings recorded with a microphone in the ear canal on the clicking side were often well above 650 Hz and always in a significantly higher area (range 117–1922 Hz, P < 0·05 or lower) than in recordings obtained with contact sensors (range 47–375 Hz) or in microphone recordings from the opposite ear canal (range 141–703 Hz). Future studies are required to establish normative frequency range values of TMJ sounds but need methods also capable of recording the high frequency vibrations. [ABSTRACT FROM AUTHOR]

For analysis of time-varying signals such as the TMJ sounds, it is often desirable to know how the frequency components change with time, using methods of time-frequency analysis. The aim of this study was to compare two of the most familiar methods for energy density representation with a newly developed technique. The sounds were recorded with a microphone fastened to the subject's forehead, transformed to the time-frequency domain and displayed as 3D- and contour plots using spectrogram, Wigner distribution (WD), and the reduced interference distribution (RID) to display their time-frequency energy distributions. The spectrogram resolved only the low-frequency components. The WD provided higher resolution but also exhibited strong interference between components. The RID gave a detailed representation of the TMJ signals' relative energy distribution in the time and frequency domains, with a great reduction in the interference or cross terms. The RID therefore appears to be most useful in the application of time-frequency distributions in classification of TMJ sounds. [ABSTRACT FROM AUTHOR]