Quantitative description of temporomandibular joint sounds: defining clicking, popping, egg shell...

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


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.



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



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.



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



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).



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.



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 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-



Else if 5á0 <maximum energy £100á0, then

If minimum interval £125 ms, then

If mean reciprocal period <700á0 Hz, then




Else

If minimum interval £125 ms, then recording is a

click


Else if 4 sound events, then




If mean reciprocal period <800á0 Hz, then record-



Else if 5á0 <maximum energy £35á0, then

If maximum reciprocal period <500á0 Hz, then



Else

If maximum interval >450á0 ms, then recording is

a click


Else if >4 sound events, then


If maximum reciprocal period <500á0 Hz, then



Else

If maximum interval >450á0 ms, then recording is

a click




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