A pilot study of nocturnal temporalis muscle activity in TMD diagnostic groups of women

Abstract

Temporomandibular disorder (TMD) incidences are believed to be related to parafunctional behaviours like teeth clenching. This pilot study aimed to (i) develop an automated clench-detection algorithm, and (ii) apply the algorithm to test for differences in nocturnal clenching in women with and without TMD. Subjects gave informed consent to participate. Adult women were categorised using Diagnostic Criteria for TMD according to presence/absence (+/-) of both TM joint disc placement (DD) and chronic pain (P) into two groups (+DD+P, -DD-P) with 12 subjects each. Surface temporalis electromyography was recorded during oral tasks performed by subjects at two laboratory sessions. The data were used to characterise muscle activity per N of bite force (μV/N) for each subject, develop the clench-detection algorithm and test its accuracy. Ambulatory surface temporalis electromyography was self-recorded by each subject over three nights and analysed using the algorithm and bite force (N) versus muscle activity μV/N calibrations. Bonferroni-adjusted homoscedastic t-tests assessed for significant between-group differences in clenching (P < 0·05). Sensitivity, specificity and accuracy of algorithm-detected laboratory clenches were all ≥96%. During self-recordings 95% of clenches had durations of <4 s and peak forces of <10 N in both groups. Mean clench durations were significantly longer (P = 0·042) in +DD+P (1·9 ± 0·8 s) than -DD-P subjects (1·4 ± 0·4 s). Mean temporalis duty factors (%clench time/total recording time) were significantly larger (P = 0·041) in +DD+P (0·47 ± 0·34%) than -DD-P (0·26 ±0·22%) subjects. Nocturnal temporalis muscle activities detected by a validated algorithm were longer per clench and recording time in +DD+P compared to -DD-P women.

Keywords: electromyography; masticatory muscles; pattern recognition; sleep bruxism; temporomandibular joint disorders; women.

Figure 1

Steps to develop the clench detection algorithm: (a) EMG activity (mV) versus time (seconds) from the 15 predefined tasks performed by a subject during one laboratory session; (b) envelopes of each of the 15 tasks after normalization to RMS EMG of Task 15 (maximum clench), rectification and low-pass filtering with amplitude threshold (σ) shown in dashed lines; (c) square waves of each of the 15 tasks where values of 0 and 1 correspond to signals with envelopes smaller and larger, respectively, than the threshold.

Figure 2

An event list data file. (a) The event list data file was constructed by concatenating the EMG recordings of 15 tasks (black) and background activity (orange). (b) Two detected clench events were identified.

Figure 3

Distribution of number of clench episodes per night by (a) duration time and (b) bite-force.