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This study presents a quantitativedescription of tempromandibular joint (TMJ)sounds provided by a rule-based classification systembased on sound classification by three dentists,who listened to and classi®ed the sound recordingsas no sound, click, coarse crepitus and fine crepitus.
Citation preview
Quantitative description of temporomandibular joint sounds:
de®ning clicking, popping, egg shell crackling and footsteps
on gravel
J. K. LEADER*, J. ROBERT BOSTON², T. E. RUDY³, C. M. GRECO§,H. S. ZAKI¶ & H. B. HENTELEFF** *Department of Bioengineering, University of Pittsburgh, ²Department of Electrical
Engineering, University of Pittsburgh, ³Departments of Anesthesiology/CCM, Psychiatry, and Biostatistics, and Research Director, Pain
Evaluation and Treatment Institute, University of Pittsburgh School of Medicine, §Departments of Anesthesiology/CCM, University of Pittsburgh
School of Medicine, ¶Department of Prosthodontics, University of Pittsburgh School of Dental Medicine and **Department of Anesthesiology,
University of Pittsburgh School of Dental Medicine, Pittsburgh, Pennsylvania, USA
SUMMARYSUMMARY This study presents a quantitative
description of tempromandibular joint (TMJ)
sounds provided by a rule-based classi®cation sys-
tem based on sound classi®cation by three dentists,
who listened to and classi®ed the sound recordings
as no sound, click, coarse crepitus and ®ne crepitus.
The sounds were recorded with microphones in the
ear canal from 126 subjects during vertical opening,
digitized at 15 000 Hz, and replayed using a compu-
ter sound card and speakers. The dentists' classi®-
cation of a test set resulted in intra- and inter-tester
j values ranging from 0á71 to 0á81 and 0á61±0á73,
respectively. Pooled j values for the dentists and
the dentists plus the rules were 0á67 and 0á58,
respectively, which were not signi®cantly different
in terms of the sound features on which the rules
were based (P = 0á13). Linear discriminant analysis
showed the four TMJ sound types were signi®cantly
different (P < 0á001). The performance of the rules
was equivalent to the dentists and marginally better
than the linear discriminant functions (P = 0á08),
establishing the validity of the quantitative descrip-
tions they provide. The recording and rebroadcast
methodology produced sounds very similar to those
observed in the clinic and could be used to train
clinicians in classifying TMJ sounds.
KEYWORDS:KEYWORDS: jaw sounds, TMD, TMJ sounds, RDC/
TMD, reliability, jaw pain1
Introduction
Temporomandibular joint (TMJ) sounds have long
been associated with the diagnosis of temporomandib-
ular disorders (TMD) (Watt, 1980; Isberg, Widmalm &
Ivarsson, 1985; Gay et al., 1987; Klineberg, 1991;
Dworkin & LeResche, 1992; Sutton et al., 1992), but
not without controversy (Toolson & Sadowsky, 1991;
Wabeke et al., 1992; Tallents et al., 1993; Stohler, 1994;
Greene et al., 1998). The research diagnostic criteria
(RDC) for TMD (RDC/TMD) uses TMJ sounds exten-
sively for patient diagnosis, but the sounds are the least
reliable of the clinical signs that comprise the RDC/TMD
(Dworkin & LeResche, 1992; Leader et al., 1999). The
description of the TMJ sound types presented in the
RDC/TMD is limited, potentially leading to confusion in
identifying the sounds.
The TMJ sounds have been commonly classi®ed as
clicks, coarse (hard) crepitus, or ®ne (soft) crepitus, with
descriptions presented in terms of many different vari-
ables. The number of transient events in a sound
recording has been used to describe clicks as both single
and multiple events (Watt, 1980; Gay & Bertolami, 1987;
Widmalm et al., 1996a) and to describe crepitus as a
series of sound events (Prinz & Ng, 1996; Widmalm et al.,
1996b). The duration of a sound event has been used to
classify sounds, with clicks de®ned as brief (20±40 ms)
transient events (Gay & Bertolami, 1987; Gay et al.,
1987; Prinz & NG, 1996) and crepitus de®ned as events
with a duration of up to 600 ms (Gay & Bertolami, 1987).
ã 2001 Blackwell Science Ltd 466
Journal of Oral Rehabilitation 2001 28; 466±478
Although the actual duration of crepitus may vary, Gay
et al. (1987) reported that crepitus had a relatively
constant ratio of noise duration to movement duration
between 0á7 and 0á9. Prinz and Ng (1996) de®ned attack
time as the time between the start of the sound transient
and the local sound maximum, and they used it to
differentiate clicks and creaks (hard crepitus). They also
calculated the ratio of energy (integrated voltage) to peak
amplitude and used it to separate hard and soft crepitus.
Widmalm et al. (1996b) used the ®rst temporal
period, de®ned as the time between the signal onset
and second zero crossing to differentiate two types of
events. One type had periods >2 ms and was classi®ed
as a click. Another type had periods <1 ms and was
classi®ed as a click if there were one or two events and
crepitus if there was a series of events.
Variables have also been de®ned in the frequency
domain to describe TMJ sounds. Gay and Bertolami
(1987) reported that clicks had peaks in the power
spectrum at 1 kHz, with rapid decline at higher fre-
quencies and crepitus had peaks in the power spectrum
at 1 kHz, but with little decline at higher frequencies.
Using reduced interference distribution (RID) time-
frequency analysis, Widmalm et al. (1996a) described
three types of clicks and two types of crepitus. Clicks had
single or a few peaks in the time-frequency spectrum
with types 1, 2 and 3 having frequency peaks 20±
600 Hz, 600±1200 Hz and 1200±3200 Hz, respectively.
Crepitus had multiple peaks with types 4 and 5 having
frequency peaks 20±600 Hz and 20±3200 Hz, respect-
ively. Gay and Bertolami (1987) indicate that click and
crepitus can be differentiated by frequency, while
Widmalm et al. (1996a) reported different types of clicks
and crepitus across the entire frequency spectrum.
Classifying TMJ sounds by listening to sound record-
ings is an under-utilized methodology. This approach is
similar to the clinical process of TMJ sound evaluation
and provides greater ecological validity than detailed
examination of brief segments of the sound. Eriksson
et al. (1987) recorded TMJ sounds from 28 patients onto
tape from a microphone inserted into the tube of a
stethoscope placed over the zygomatic bone. From the
tape, 11 staff members and 11 students classi®ed the
records as no sound, click or crepitus. Mean intra-
observer agreement was 79% and the mean j values
were 0á6 and 0á7 for the students and staff, respectively.
Only four patients (14%) were classi®ed the same by all
22 observers, and 18 patients (64%) were classi®ed
the same by 12±21 observers (inter-observer j values
unreported). Milner et al. (1991) recorded TMJ sounds
from 20 joints onto tape from a microphone inserted
into the tube of a stethoscope placed over the TMJ.
A stimulus tape was made for replaying the sounds in
random order. Six dental specialists and eight dental
students listened to and classi®ed the sounds as none,
click/pop, soft crepitus or hard crepitus. Mean inter-
observer agreement was 43á8 and 49á8% for the
students and dentists, respectively.
As suggested by the number of time and frequency
sound features that have been proposed for classi®ca-
tion, TMJ sounds are complex and it is unlikely that
they can be classi®ed by linear discriminant functions
based on one or a few features. An alternative method
to describe how features are used for classi®cation is to
build a rule-based classi®cation system. The rules,
which can account for complex interactions between
the features, can provide a quantitative description of
how the features relate to the different classi®cations.
Several studies have described automated classi®ca-
tion of TMJ sounds. Prinz and Ng (1996) developed an
automated classi®cation system that used time domain
parameters and was evaluated against ®ve observers
who visually classi®ed sounds as silence, click, creak,
crepitus, click combined with crepitus or don't know.
The automated system used the following criteria: clicks
had a duration of <30 ms and an attack of <10 ms, creaks
(hard crepitus) had a ratio of energy to peak amplitude
>40 and an attack >15 ms, and crepitus (soft crepitus)
had a ratio of energy to peak amplitude >40. Two studies
examined automated identi®cation of Widmalm's ®ve
RID time-frequency sound types. Brown et al. (1994)
used a classi®cation algorithm based on Renyi's entropy
to separate clicks from crepitus and the adaptive gabor
transform (AGT) to separate the three type of clicks. The
system was evaluated by visual analysis of the wave-
forms. Yang et al. (1998) used three pattern recognition
techniques to identify Widmalm's three types of clicks:
(1) nearest neighbour (NN), a non-parametric method;
(2) nearest linear combination (NLC), in which the
nearest distances were examined in linear subspace; (3)
and nearest constrained linear combination (NCLC), in
which the nearest distances were examined in a con-
strained linear subspace.
The studies described above have followed one of the
two approaches to describe TMJ sounds. One approach is
to establish a subject's TMD diagnosis and subsequently
describe the qualitative or quantitative characteristics of
their sounds. The other approach is to visually classify
Q U A N T I T A T I V E D E S C R I P T I O N O F T M J S O U N D S 467
ã 2001 Blackwell Science Ltd, Journal of Oral Rehabilitation 28; 466±478
the TMJ sounds from displayed waveform recordings
and subsequently describe the qualitative or quantitative
characteristics of the sounds. Only two of the studies
(Eriksson et al., 1987; Milner et al., 1991) had the
examiner classifying TMJ sounds by listening to labor-
atory recordings and these studies did not describe any
sound features speci®c to different classi®cations.
A quantitative description of the different TMJ
sounds types, in which the clinicians have classi®ed
sounds by listening to TMJ sound recordings, appears to
be absent in the research literature. A complete and
concise description would provide clinicians with tools
for explaining the sound types to other clinicians and
might increase the reliability of classi®cation. To pro-
vide ecological validity to such a quantitative descrip-
tion, it should be derived from a process similar to the
clinical evaluation of sounds. To mimic the clinical
process in the laboratory, evaluators should classify and
develop quantitative descriptions by listening to record-
ings of the TMJ sounds.
The purpose of this study was to use a rule-based
classi®cation system to provide a quantitative descrip-
tion of different TMJ sounds types of no sound, click,
coarse crepitus, and ®ne crepitus. Sounds recorded in
the laboratory were classi®ed into the four sound types
by three dentists and the reliability of the classi®cation
was evaluated. We hypothesized that the TMJ sound
recording and rebroadcast methodology would produce
sounds that were suf®ciently similar to the clinical
TMJ sounds to permit the dentists to reliably classify the
TMJ sound recording. Sound features were calculated
from the waveforms to form rules and to identify
examples of the sounds. The rules were applied to a test
set of sounds also classi®ed by the dentists. We also
hypothesized that the rule-based classi®cation system
would be consistent with the dentists' classi®cation of
the sound recordings. Sound recordings on which the
dentists' disagreed on classi®cations were also analysed
to determine if the dentists themselves or speci®c sound
features caused the disagreement.
Methods
Subjects
One hundred and twenty-six consecutive patients
admitted to the TMD clinic at the University of
Pittsburgh Pain Institute with the diagnosis of TMD,
as determined RDC/TMD (Dworkin & LeResche, 1992),
served as subjects in this study. Subject were com-
pletely informed of the bene®ts and risks associated
with the experimental procedures and provided writ-
ten informed consent, approved by the biomedical
Internal Review Board. The TMD patients met the
following inclusion criteria: (1) were between 18 and
60 years of age; (2) had jaw pain of at least 3 months
duration; (3) had no history of surgical treatment for
TMD problems; (4) had no periodontal disease or
poorly ®tted dentures that would mimic TMD symp-
toms and (5) had no third molar problems, such as
pericoronitis or supereruption. Basic subject demo-
graphics and RDC/TMD diagnostic classi®cations,
including the percentage of patients with multiple
diagnoses, are presented in Table 1.
Experimental procedures
Overview. The study began by recording TMJ sounds
produced by the subjects during maximum vertical
opening. Next, the number and temporal relation of the
sound events present in the recordings were identi®ed
by a computer algorithm and were used to place the
recordings into one of the ®ve categories. From each
category, 15 recordings were randomly selected to form
a training and test set, each with 75 recordings. The
sound recordings of the training and test set were
randomly played on separate days for three dentists
who listened to and classi®ed the sounds into one of
four sound types. Sound features identi®ed from the
dentists' classi®cation of the training set were used by
the ®rst author to develop an automated rule-based
classi®cation system to classify TMJ sound recordings.
The rules were then applied to the test set to evaluate
their performance. Details of these steps are provided
below.
Sound data collection. The TMJ sounds produced during
maximum vertical jaw opening were recorded bilater-
ally with Panasonic electret condenser microphones
placed in the external ear canal and held in place with
silicone putty. Two sizes of microphones, 9á7 mm in
diameter (model WM-034CY195) and 6á0 mm in
diameter (model WM-60AY), were used for varying
ear canal sizes. The microphones had ¯at frequency
response from 20 to 16 000 Hz. Microphone output
was ampli®ed with a gain of 100 and ®ltered from 100
to 5000 Hz (3 dB frequencies). The resulting analogue
signal was digitized at 15 kHz with 12-bit resolution
J . K . L E A D E R et al.468
ã 2001 Blackwell Science Ltd, Journal of Oral Rehabilitation 28; 466±478
using ASYST software*. Using open-®eld recording
conditions, microphone output voltage was calibrated
to dB Sound Pressure Level (dB SPL) with the help of a
sound level meter and an audiometric calibrator².
Sound data analysis. The TMJ sounds appear as single or
multiple groups of spikes in the sound recordings.
Spikes with amplitude greater than 65 dB SPL were
considered to be signi®cant and were called `sound
events'. This amplitude level was suf®ciently greater
than the baseline noise which minimized the possibility
of background noise being identi®ed as a sound event.
A single spike without a neighbouring spike within a
100 ms window was de®ned as a simple sound event. A
single spike and a neighbouring spike within a 100 ms
window were together de®ned as a complex sound
event.
Sound stimulus criteria. All the sound events in the TMJ
sound recordings recorded from the subjects were
categorized as simple or complex by a computer
algorithm. Each sound recording was then placed in
one of the ®ve categories: no sound, single simple
event, multiple simple events, single complex event or
multiple simple/complex events. Fifteen sounds from
each of these ®ve categories, for a total of 75, were
randomly selected to form a training set. This set of
sounds was used to develop an automated rule-based
classi®cation system. An additional set of 75 sounds was
randomly selected using the same criteria, to form a test
set that was used to validate the rules. The rules were
completely developed on the training set and then
applied to the test set to avoid biasing the rules to the
data used for testing. The rule-based classi®cation
system was developed using the individual sound
events rather than the simple and complex categories.
Sound stimulus presentation. The TMJ sound recordings
from both data sets were played to three RDC-calibrated
dentists, using a Sound Blaster 16 plug-n-play sound
card³ and Satellite 31 speakers§ on a Dimension XPS
P166s personal computer¶. All sounds were played at
the same volume settings and repeated at the testers'
request. The dentists classi®ed the recordings as no
sound, click, coarse crepitus or ®ne crepitus, with the
types being mutually exclusive. On 3 different days,
separated by at least 1 week, the dentists classi®ed the
training set once and the test set twice.
Data analysis
To evaluate the dentists' intra- and inter-tester reliab-
ility, and to compare the rule-based classi®cation sys-
tem to dentists, j values and percent agreements were
Table 1. TMD subjects' demographic and pain-related descriptive
statistics
Gender
Male 29
Female 97
Age (years)
Mean 30á2s.d. 7á9
High school graduate (%) 98
Marital status (%)
Single 49
Married 43
Separated/divorced 8
Full time employment (%) 46
Duration of pain (in years)
Mean 6á9s.d. 6á8
Constant pain (%)
Yes 45
No 55
Pain onset (%)
Gradual 57
Abrupt 43
Reported analgesic usage (%)
Any 79
3 or more days per week 38
Number of previous treatments for TMD
Mean 1á9s.d. 2á4
RDC/TMD axis I diagnoses (%)
Group I (myofascial) 31á7Group II (disc displacement) 3á0Group III (arthralgia, osteoarthritis, osteoarthrosis) 2á0Groups I and II 20á8Groups I and III 23á8Groups I, II and III 18á8
*ASYST, Rochester, New York, USA.²GenRad, Concord, Massachusetts, USA.
³Creative Labs, Milpitas, California, USA.§Altec Lansing, Milford, Pennsylvania, USA.¶Dell, Round Rock, Texas, USA.
Q U A N T I T A T I V E D E S C R I P T I O N O F T M J S O U N D S 469
ã 2001 Blackwell Science Ltd, Journal of Oral Rehabilitation 28; 466±478
calculated. The two independent classi®cations of the
test set by each dentist were used to determine intra-
tester reliability. Intertester reliability was calculated for
each pair of dentists from the classi®cation of the
training set and the ®rst classi®cation of the test set. jValues were also calculated for the ®rst classi®cation of
the test set pairing each dentist with the rule-based
system. Pooled j values were calculated for the classi-
®cation of the test set by the three dentists and by the
same plus the rules. The difference between the pooled
j values were tested using a chi-square distribution
with one degree of freedom, based on procedures
developed by Fleiss (1981).
To analyse the dentists' classi®cation of the TMJ
recordings, the test and training sets were divided into
two samples, majority agreement and disagreement.
The training set was classi®ed once by the three
dentists, with two out of three identical classi®cations
being considered as majority agreement. The test set
was classi®ed twice by three dentists, with four out of
six identical classi®cations being considered as majority
agreement.
Four sound features identi®ed from the dentists'
classi®cation of the majority agreement sample of the
training set were used to describe the sounds: (1)
number of sound events (2) energy in each sound event
(3) time interval separating sound events, and (4)
reciprocal of the ®rst temporal period of each sound
event. The energy in a sound event was calculated as
the sum of the squared sound amplitude samples
during the event. The time interval separating the
events was the time difference between the start of each
event in milliseconds. The time of the ®rst temporal
period of a sound event (events with single or multiple
periods) was used to estimate the frequency content.
The reciprocal of the ®rst temporal period was calcu-
lated and will be described as frequency and reported in
hertz. The time intervals were summarized as maxi-
mum and minimum during an opening, and the
reciprocal periods were summarized as maximum and
mean values. The rule-based classi®cation system used
six sound features, including number of events, maxi-
mum energy, maximum time interval, minimum time
interval, maximum reciprocal period and mean reci-
procal period.
The sound features of the rule-based classi®cation
system were also used to analyse the majority agree-
ment and disagreement samples by linear discriminant
analysis. First, linear discriminant analysis of the
majority agreement sample was used to test if the four
sound types were signi®cantly different using the six
features as dependent variables. Secondly, linear clas-
si®cation functions were derived from these six vari-
ables for each sound type and the in¯uence of
removing each feature from the functions was meas-
ured using an F-test. Thirdly, the classi®cation of the
majority agreement sample by the rules was compared
with classi®cation by the linear classi®cation functions.
Finally, each member of the majority disagreement
sample was analysed in terms of their Mahalanobis
distances from the centroids of the four sound types.
Rule-based classi®cation system development
The rule-based classi®cation system consisted of a series
of if ¼ then ¼ else ¼ statements forming a decision
tree that captured the separation of the six sound
features described above. Derivation of the sound
features was based on the dentists' classi®cation of the
training set. Sound feature values and histograms of
the features values were calculated for the recordings of
the majority agreement sample of the training set.
Rules based on these histograms were formed by the
®rst author to produce the most successful classi®cation
of the training set. When the performance of the rules
was considered satisfactory, the rules were applied to
the 75 sounds of the test set. A complete listing of the
rules is included in the Appendix.
Results
Sound waveform examples
Figures 1, 2 and 3 each show three examples of clicks,
coarse crepitus and ®ne crepitus, respectively, from the
test set. The dentists (both classi®cations of the test set)
and rule-based classi®cation system agreed on the
classi®cation of all of these sounds.
A stereotypical TMJ click with one sound event is
shown in Fig. 1A. Figure 1B and 1C show multiple
event recordings classi®ed as clicks because the events
were relatively close and relatively distant in time,
respectively.
The recording in Fig. 2A had two sound events close
in time, but was classi®ed as coarse crepitus because
both events had low energy and moderate frequency,
without a single event dominating the recording. The
series of click-like events separated by moderate time
J . K . L E A D E R et al.470
ã 2001 Blackwell Science Ltd, Journal of Oral Rehabilitation 28; 466±478
intervals shown in Fig. 2B was classi®ed as coarse
crepitus. Figure 2C shows a coarse crepitus recording
with a series of 14 sound events all with low energy
which produced an extended crackling noise.
The ®ne crepitus example in Fig. 3A had one sound
event with very low energy and very high frequency.
A high pitched crackling sound was generated by
transients spikes that were below the threshold used
in this study to de®ne sound events. Figure 3B and C
are multiple sound event recordings classi®ed as ®ne
crepitus, with low energy and very high frequency
resulting in low amplitude high pitched crackling.
Classi®cation reliability
Intra-tester reliability. Intra-tester j values for classi®ca-
tion reliability are presented in Table 2. Intra-tester
reliability and agreement were calculated from the
dentists' two independent classi®cations of the test set.
Intra-tester reliability was good, with dentists 1, 2 and 3
producing intra-tester j values of 0á81, 0á72 and 0á71,
respectively. The intra-tester complete agreements on
the two classi®cations for dentists 1, 2 and 3 were 86á7,
81á3 and 80á0%, respectively.
Intertester reliability. Table 2 also shows the intertester
reliability calculated from the dentists' ®rst classi®ca-
tion of the test set and their classi®cation of the training
set. The pairings of dentists 1 and 2, 1 and 3, and 2 and
3 produced intertester j values of 0á73, 0á61, 0á65
respectively on the test set and 0á75, 0á71 and 0á59
respectively on the training set. Pooled j values for
classi®cation of the test set by the three dentists was
0á67 and for classi®cation of the training set by the
three dentists was 0á68. The complete agreement of the
three dentists was 68á0% on the ®rst classi®cation of
the test set, 65á3% on the second classi®cation of the
test set and 68á8% on the training set.
Rule-based classi®cation system
Histograms of the TMJ sound features were constructed
from the dentists' classi®cation of the training set
and used to develop the branches of the rule-based
classi®cation system tree. The primary branch of the
rules was the number of sound events present in the
recordings. Figure 4A shows the number of sound
events of histogram and revealed the following patterns:
Fig. 1. Three examples of TMJ sound recordings classi®ed as
clicks. (A) One sound event classi®ed as a click, (B) and (C)
multiple sound events classi®ed as clicks.
Fig. 2. Three examples of TMJ sound recordings classi®ed as
coarse crepitus. (A) Two sound events classi®ed as coarse crepitus,
(B) and (C) a series of clicks-like sound events classi®ed as coarse
crepitus.
Q U A N T I T A T I V E D E S C R I P T I O N O F T M J S O U N D S 471
ã 2001 Blackwell Science Ltd, Journal of Oral Rehabilitation 28; 466±478
no sound events ± no sound present or ®ne crepitus,
1 sound event ± no sound, click, or ®ne crepitus,
2 sound events ± click, coarse crepitus, or ®ne crepitus,
3 sound events ± click, coarse crepitus, or ®ne crepitus,
4 sound events ± click or coarse crepitus,
>4 sound events ± click or coarse crepitus.
Histograms of the ®ve remaining sound features
(maximum energy, maximum time interval, minimum
time interval, maximum reciprocal period, mean
reciprocal period) were constructed for the last ®ve
subgroups based on the number of sound events.
The secondary branch of the rules was based on the
maximum energy present in the sound events grouped
as low, moderate and high. In general, no sound and
®ne crepitus recordings possessed very low energy,
while clicks had moderate to high energy, as shown in
Fig. 4B. The time interval separating sound events or
the reciprocal period formed the tertiary branch of the
rules. Figure 4C is the histogram of the maximum time
intervals separating sound events, and Fig. 4D is the
histogram of the minimum time intervals separating
sound events. These histograms showed that clicks
were either separated by small or large time intervals,
while coarse and ®ne crepitus showed no patterns,
except that sound events in coarse crepitus have a small
minimum separation interval. The histograms of maxi-
mum and mean frequency associated with the recipro-
cal of the ®rst period, Fig. 4E and 4F, respectively,
illustrate that clicks have low to moderate frequency
while coarse and ®ne crepitus have moderate to high
frequency.
Dentist and rule-based system reliability
Intertester reliability was calculated for the dentists
paired with the rule-based classi®cation system on the
test set. The j values between dentist 1 and rule system,
dentist 2 and rule system, and dentist 3 and rule system
were 0á50, 0á56, 0á41, respectively. The dentists and the
rule system had a 49á3% complete agreement on both
the ®rst and second classi®cation of the test set. Pooled
j values for classi®cation of the test set by the three
dentists plus the rule system was 0á58. The pooled jvalues for the classi®cation of the test set by the three
dentists (0á67) and the dentists plus the rules were not
signi®cantly different (v2(1) � 2á36, P � 0á13).
Linear discriminant analysis
The dentists reached a majority agreement on a total of
138 sound recordings, 64 from the test set and 74 from
the training set. They disagreed on 12 recordings. A
linear discriminant analysis of the six sound features
used in the rule-based classi®cation system was applied
to the majority agreement sample, using the four sound
Fig. 3. Three examples of TMJ sound recordings classi®ed as ®ne
crepitus. (A) One sound event classi®ed as ®ne crepitus, (B) and
(C) a series of click-like sound events classi®ed as ®ne crepitus.
Test set of sound recordings Training set of sound recordings
Dentist 1 Dentist 2 Dentist 3 Dentist 1 Dentist 2 Dentist 3
Dentist 1 0á81 ± ± ± ± ±
Dentist 2 0á73* 0á72 ± 0á75 ± ±
Dentist 3 0á61* 0á65* 0á71 0á71 0á59 ±
Rule system 0á50* 0á56* 0á41* ± ± ±
*First classi®cation of the test set.
Table 2. j Values for intra- and
inter-tester reliability of dentists and
rule-based classi®cation system
J . K . L E A D E R et al.472
ã 2001 Blackwell Science Ltd, Journal of Oral Rehabilitation 28; 466±478
Q U A N T I T A T I V E D E S C R I P T I O N O F T M J S O U N D S 473
ã 2001 Blackwell Science Ltd, Journal of Oral Rehabilitation 28; 466±478
types classi®ed by the dentists as groups. Table 3 shows
the mean and standard deviations of the six features for
the majority agreement sample grouped into the
dentists' classi®cation. Wilks' k showed that at least
two of the four sound types were signi®cantly different
on the six sound features (k � 0á28, F(18, 365) � 11á62,
P < 0á001). Further pairwise analysis revealed that all
four group centroids were signi®cantly different from
each other as shown by F-test results in Table 4. Linear
classi®cation functions were calculated for the four
groups and the in¯uence of removing the sound
features individually was calculated. The number of
sound events and maximum energy were the most
in¯uential features, with both being statistically signi-
®cant (P < 0á001). The linear classi®cation functions
correctly classi®ed 72% of the majority agreement
sample and the automated rule-based classi®cation
system correctly classi®ed 82% of the same sample. The
rules were marginally better than the linear classi®ca-
tion function (v2(1) � 2á96, P � 0á08).
The linear classi®cation functions were also used to
classify the disagreement sample, and the Mahalanobis
distances from the group centroids were calculated for
all recording. Finally, the mean Mahalanobis distances
from the group centroids were calculated for the
majority agreement sample, yielding 0á55, 7á40, 6á24
and 6á84 for no sound, click, coarse crepitus and ®ne
crepitus sound type groups, respectively. The mean
Mahalanobis distances for the disagreement sample
were 0á45, 4á61, 5á34 and 1á56 for no sound, click,
coarse crepitus and ®ne crepitus sound type groups,
respectively, approximately the same as for the major-
ity agreement sample. The 12 majority disagreement
recordings were classi®ed by the linear classi®cation
functions as: no sound � 3, click � 1, coarse crep-
itus � 4 and ®ne crepitus � 4, and the rules classi®ed
them as: no sound � 1, click � 2, coarse crepitus � 5,
and ®ne crepitus � 4. Only two recordings were clas-
si®ed differently by the two systems. As both samples
were equally close to the group centroids and were
classi®ed essentially the same by the rules and the
classi®cation functions, the disagreement and agree-
ment recordings appear to be similar in terms of the
sound features.
Fig. 4. Histograms of sound features of the training set used to
develop the rule-based classi®cation system. The recordings were
grouped into the four sound types based on classi®cation by three
dentists. (A) Number of sound events in the recording, (B)
maximum energy in the events, (C) and (D) maximum and
minimum time interval separating sound events, respectively, (E)
and (F) maximum and mean, respectively, of the reciprocal of the
®rst temporal period used to estimate frequency.
No sound Click Coarse crepitus Fine crepitus
Number of recordings 24 68 29 17
Number of sound 0á29* 2á79 5á76 1á94
events (0á54) (1á79) (3á92) (1á00)
Maximum² 0á29 121á145 41á51 3á53
energy³ (0á61) (115á17) (88á35) (7á73)
Maximum 16 258 310 196
interval (ms) (77) (389) (251) (219)
Minimum 16 139 53 88
interval (ms) (77) (367) (63) (120)
Maximum 163á5 710á9 1040á6 1817á6frequency§ (Hz) (424á0) (466á0) (369á9) (1340á4)
Mean 161á1 456á4 692á9 1446á7frequency (Hz) (423á4) (267á3) (284á4) (1085á8)
*Mean values, standard deviation (); ²maximum, minimum, and mean operation performed
when two or more values of sound feature exist; ³sum of squared values; §estimated from the
reciprocal of the ®rst temporal period.
Table 3. Sound features separated
into the dentists' classi®cations of
sound types
Table 4. Between groups F-matrix*
No sound Click
Coarse
crepitus
Fine
crepitus
No sound 0á00 ± ± ±
Click 8á61 0á00 ± ±
Coarse crepitus 15á19 12á03 0á00 ±
Fine crepitus 12á43 12á69 10á71 0á00
*F(6,129).
J . K . L E A D E R et al.474
ã 2001 Blackwell Science Ltd, Journal of Oral Rehabilitation 28; 466±478
Discussion
Sound features used by three RDC/TMD calibrated
dentist to classify TMJ sounds types of no sound, click,
coarse crepitus, and ®ne crepitus were identi®ed and
quanti®ed in the present study. Clicks consisted of a
single sound event or multiple events (either relatively
close or relatively distant temporally) with high energy
and low frequency. Coarse crepitus is a series of click-
like sound events with moderate energy and moderate
to high frequency. Fine crepitus is a series of sounds
events with low energy and high frequency. Six sound
features were used in this study to describe the sounds:
the number of sound events in the recording, maxi-
mum energy in the events, maximum and minimum
time interval separating events, and maximum and
mean of the reciprocal of the ®rst period (estimate of
frequency). An automated rule-based classi®cation
system, based on these features, was consistent with
the three dentists in classifying TMJ sound recordings,
as hypothesized, indicating that the features captured
the essence of the dentists' classi®cation process. The
rules provide a quantitative description of the TMJ
sound types.
The TMJ sounds analysed in this study were recorded
from microphones in the external ear canal and
rebroadcast using the sound card and speakers of a
personal computer. As hypothesized, the dentists
reported a strong similarity between the TMJ sounds
played in this study and those heard from a stethoscope
during clinical examination. This was con®rmed by the
good to excellent intra- and inter-tester j values for
reliability of classi®cation of the sounds observed in the
study. With the classi®cation reliability established, the
dentists' classi®cation process was examined to identify
the sound features of the recordings they utilized in
classi®cation.
Several sound features of the TMJ sound types were
identi®ed from the dentists' classi®cation of the sound
recordings. The classi®cation type TMJ click implies a
single sound event, but like other investigations, this
study described a TMJ click as a recording containing
single or multiple events. Watt (1980) noted that hard
clicks consist of 2 or 3 cracks occurring together, and
Widmalm et al. (1996a) classi®ed clicks as single or a
few peaks occurring at different times in the time-
frequency spectrum. The complex doublet clicks
observed by Gay and Bertolami (1987) were also
observed in the present study (Fig. 1B). However, if
the doublets were close in time and equal in the
physical properties of energy and frequency, they could
be classi®ed as crepitus (Fig. 2A).
Crepitus has been de®ned as both a sound event of
long duration (Gay & Bertolami, 1987), a series of
events (Prinz & Ng, 1996) and as a series of events
separated by <10 ms (Widmalm et al., 1996b). Con-
¯icting reports have been published for describing the
individual events of the series. Gay et al. (1987)
suggested that multiple transients produced by
patients with a non-reducing displaced disc would be
auditorily indistinguishable from crepitus. Widmalm
et al. (1996b) de®ned crepitus as a series of clicks.
However, Prinz and Ng (1996) reported creaks and
crepitus to be a series of sounds, with the character-
istics of the individual sounds of the series having
signi®cantly different characteristics from clicks. The
present study found that both coarse and ®ne crepitus
recordings were a series of click-like events and actual
classi®cation depended on other features of the sound
waveform.
In general, sound events comprising TMJ clicks were
observed to have more energy and lower frequency
than those of crepitus, which is comparable with what
others have observed. Gay et al. (1987) observed
that clicks had signi®cant energy in the low frequencies
while crepitus had energy throughout the frequency
spectrum. Widmalm et al. (1996b) classi®ed sounds
with long temporal period, indicating low frequency, as
clicks and sounds with short temporal periods, indica-
ting high frequency, as clicks or crepitus.
The interactions of sound events are complex,
making interpretation of them dif®cult, so a set of rules
was developed to capture the complex interaction.
Classi®cation of the sounds by the dentists and the rules
were not signi®cantly different from each another and
fair reliability was observed between the dentists and
the rule-based classi®cation system. Prinz and Ng
(1996) presented an automated classi®cation technique
that was compared with visual classi®cation of TMJ
waveforms and reported a slightly better agreement
with a human observer than was found in the present
study. The classi®cation of sounds by their automated
system compared with one observer resulted in a jvalue of 0á71 and an 82% agreement. Like the present
classi®cation system, their automated technique was
constructed from sound features in the time domain,
which they suggest are more appropriate than the
frequency domain.
Q U A N T I T A T I V E D E S C R I P T I O N O F T M J S O U N D S 475
ã 2001 Blackwell Science Ltd, Journal of Oral Rehabilitation 28; 466±478
Linear discriminant analysis based on the six sound
features showed that the four TMJ sound types, as
classi®ed by the dentist, were signi®cantly different
from one another in terms of the six features used here.
The number of sound events and maximum energy
were the most in¯uential features of the six. The rules
performed better than a set of classi®cation functions
derived from the linear discriminant analysis, indicating
that the sound features interact with one another. The
rule-based classi®cation system is more appropriate
than a linear equation for capturing the complex
interactions of the sound events.
Although the dentists are trained to process these
complex sound interactions, there were sounds that
caused confusion among them. The sound recordings
on which the dentists disagreed were classi®ed almost
identically by the rule-based classi®cation system and
the linear classi®cation functions. Examining the
Mahalanobis distances from the group centroids of the
four sound types revealed that the disagreement sound
recordings were as close to the centroids as the
agreement sound recordings, suggesting that disagree-
ments were not caused by confounding features present
in the sound recordings. It was concluded that the
disagreement sound recordings were simply improperly
classi®ed by one of the dentists.
The present study had good to excellent intra-tester
reliability, equivalent to that of Eriksson et al. (1987),
and had good intertester agreement that was much
better than Eriksson et al. (1987) and Milner et al.
(1991). Like Milner et al. (1991), the current study
observed better performance on the no sound type
compared with the others. However, unlike Milner et al.
(1991), who found no relation between sound features
and examiner classi®cation, we found a de®nite relation
between the sound features and dentists' classi®cation
as supported by the success of the automated rule-based
classi®cation system. Eriksson et al. (1987), who re-
played recordings from tape, noted the potential for
background noise to contribute to poor performance,
especially in the increase in reports of crepitus. Sounds
are greatly in¯uenced by the medium which captures
and presents them. Therefore, caution must be taken
when comparing results between studies.
A limitation of the present study is that it was
performed with three dentists on one subject popula-
tion. To establish the robustness of the quantitative
descriptions of the TMJ sound types and sound feature
values of the automated rule-based classi®cation sys-
tem, sounds need to be evaluated by other dentists and
applied to other subject populations. A different subject
population would allow further development of the
rules and removal of the speci®city inherent from a
single population. The TMJ sounds are complex, but the
initial success of the rules revealed the potential for
creating an improved system.
The values of the rules were formed from the
classi®cation results of RDC/TMD calibrated dentists
who listened to the TMJ sound recordings. Our inten-
tion was to add ecological validity to the laboratory
classi®cation by mimicking the classi®cation process of
the clinic. Instead of performing a microscopic analysis
of individual sound events, the sound features and
patterns of the entire sound recording were examined.
The sound recordings could help train clinicians in TMJ
sound classi®cation and would likely improve the
reliability among clinicians for using TMJ sounds in
the diagnosis of TMD. The automated rule-based
classi®cation system presented here should assist
researchers in the consistent and ef®cient identi®cation
of TMJ sound recordings. The quantitative description
of TMJ sound types, enumerated in the rule-based
classi®cation system, should facilitate a standardization
of TMJ sound classi®cation.
Acknowledgments
The authors would like to thank Dr Anna Pergamalian
for her participation in the classi®cation of the TMJ
sound recordings. This work was supported by grant
(R01 DE07514) from the National Institute of Dental
Research, National Institutes of Health, Bethesda, M.D,
20892, USA.
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Appendix
Algorithm of the automated rule-basedclassi®cation system
If no sound events, then recording is no sound
Else If 1 sound event, then
If energy >1á5, then recording is a click
Else
If reciprocal period >800á0 Hz, then recording is
no sound
Else recording is ®ne crepitus
Else If 2 sound events, then
If maximum energy £35á0 and interval £400á0 ms,
then
If mean reciprocal period £800á0 Hz, then record-
ing is coarse crepitus
Else recording is ®ne crepitus
Else if maximum energy ³35á0, then
If interval £125á0 ms or interval ³400á0 ms, then
recording is a click
Else recording is coarse crepitus
Else recording is a click
Else if 3 sound events, then
If maximum energy £5á0, then
If mean reciprocal period <700á0 Hz, then record-
ing is coarse crepitus
Else recording is ®ne crepitus
Else if 5á0 <maximum energy £100á0, then
If minimum interval £125 ms, then
If mean reciprocal period <700á0 Hz, then
recording is a click
Else recording is coarse crepitus
Else recording is coarse crepitus
Else
If minimum interval £125 ms, then recording is a
click
Else recording is coarse crepitus
Else if 4 sound events, then
If maximum energy £5á0, then
Q U A N T I T A T I V E D E S C R I P T I O N O F T M J S O U N D S 477
ã 2001 Blackwell Science Ltd, Journal of Oral Rehabilitation 28; 466±478
If mean reciprocal period <800á0 Hz, then record-
ing is coarse crepitus
Else recording is ®ne crepitus
Else if 5á0 <maximum energy £35á0, then
If maximum reciprocal period <500á0 Hz, then
recording is a click
Else recording is coarse crepitus
Else
If maximum interval >450á0 ms, then recording is
a click
Else recording is coarse crepitus
Else if >4 sound events, then
If maximum energy £35á0, then
If maximum reciprocal period <500á0 Hz, then
recording is a click
Else recording is coarse crepitus
Else
If maximum interval >450á0 ms, then recording is
a click
Else recording is coarse crepitus
J . K . L E A D E R et al.478
ã 2001 Blackwell Science Ltd, Journal of Oral Rehabilitation 28; 466±478