Characteristics of EMG frequency bands in temporomandibullar disorders patients
Introduction
Degenerative temporomandibular joint disorders (Kopp, 1995) limited jaw joint motion (Celić et al., 2003), and masticatory muscle disorders accompanied by pain (Dworkin and LeResche, 1992, Yap et al., 2003) have been reported as the most common signs and symptoms of temporomandibular disorders (TMD). The incidence and prevalence of TMDs in different population groups has been the subject of a large number of epidemiologic studies, in general, and the signs and symptoms are severe (de Oliveira et al., 2006, Franco-Micheloni et al., 2015, Ryalat et al., 2009).
Due to the multiplicity of factors that may cause or contribute to the manifestation or worsening of TMDs, the pathophysiology of TMD is not fully understood yet; thus, the mechanisms of action of the types of treatments remain unclear (Michelotti and Iodice, 2010). Consequently, the diagnosis of TMD jeopardizes the comparison of clinical studies because the sample is not always homogeneous.
All these inherent conditions of the genesis of TMD have generated great efforts of many researchers to improve diagnostic tools, or to simply evaluate this disease. Currently, to identify the presence or absence of TMD, the gold standard is still mainly based on clinical examination (currently available standardized classification schemes) supplemented, when deemed appropriate, with imaging (magnetic resonance and, when needed, computerized tomography) (Manfredini et al., 2011).
However, although the usefulness of surface electromyography (sEMG) in diagnosing TMD is considered to be limited (Al-Saleh et al., 2012), several studies have shown that EMG activity can be used to identify different characteristics of the masticatory muscles during the performance of static and dynamic tasks in patients with TMD, and then those findings can be compared with the characteristics of normal subjects (De Felício et al., 2013, Lauriti et al., 2014, Lauriti et al., 2013). Moreover, those findings can also be used to assess the efficacy of different therapies (Amorim et al., 2012, Biasotto-Gonzalez and Bérzin, 2004, El Hage et al., 2013).
Different variables from the EMG signals can be used to quantify muscle activity. Spectral analysis and amplitude estimation are usually performed to obtain indications about the physiological and biomechanical processes that occur in the muscles used in mastication in healthy subjects and in patients with TMD (De Felício et al., 2013, Lauriti et al., 2014, Lauriti et al., 2013).
Amplitude features are frequently used only as a qualitative indication of muscle state (De Felício et al., 2013, De Felício et al., 2012). In general, in the masticatory muscles (in particular the masseter and the anterior temporalis muscles) the amplitude of the sEMG signal is normally observed during maximal clenching effort or even during chewing (De Felício et al., 2013, El Hage et al., 2013, Lauriti et al., 2013).
However, when investigating the force level and/or contraction velocity of the muscle, the EMG amplitude can be affected by factors that are not relevant to force production, such as the wave shape of the motor unit action potentials (De Luca, 1997, Farina et al., 2004). In addition, the nonlinear relationship between the size of the response and the amplitude of the EMG signal from the rectified EMG signal (Baker and Lemon, 1995, Hamm et al., 1985), and the amplitude alterations of the signal due to cancellation (Farina et al., 2008, Keenan et al., 2006), limit the possibility of using EMG amplitude as a parameter to indicate specific characteristics of the masseter and anterior temporalis muscles in TMD patients.
Another disadvantage of the time domain analyses is the need for additional recording of the sEMG signal during maximal voluntary contractions so that they can serve as a parameter for normalization of raw EMG signal. This procedure increases the time it takes to record the data and the procedure often ends up being uncomfortable for the patient.
To record the data more quickly and to also carry out a specific physiological analysis of the masseter and anterior temporalis muscles of TMD patients, applying a frequency domain analysis can be more effective. In general, the frequency shift has been attributed to changes in the recruitment and synchronization of the motor units (Solomonow et al., 1990); it also identifies the type of motor units recruited (Wakeling and Rozitis, 2004) and the type of fiber (Kupa et al., 1995, Larsson et al., 2006) and is also more sensitive to the diameter of the fibers (Bilodeau et al., 1994).
In the frequency domain, the median frequency (MDF) and the mean frequency are two parameters of the power density spectrum function (PDSF) that may be easily used to provide useful measures of the EMG frequency spectrum. However, the MDF presents the advantage of being less affected by random noise, particularly in the case of noise located in the high frequency band of the EMG power spectrum (Stulen and DeLuca, 1981). Furthermore, it is possible to use the MDF as an index to identify the recruitment control strategies employed by various muscles during different force levels (Solomonow et al., 1990).
However, the MDF is a global measure of the PDSF limited to a single frequency value of the EMG frequency spectrum. Thus, some studies have shown that frequency band analysis can reveal physiologically relevant information from the shifts in the power spectrum (Cardozo et al., 2011, Dolan et al., 1995, Ferrari et al., 2014, Kwon et al., 2012, Neto et al., 2010, Roman-Liu and Konarska, 2009).
In general, using frequency band analyses may enable an investigation to obtain more detailed information about the contributions made by the specific frequency bands of the sEMG signal. For example, the 20–50 Hz band has been described as a better indicator of fatigue rate or “fatigability” in the back muscles in relation to the MDF (Cardozo et al., 2011) and the 45–96 Hz band of the vastus lateralis and vastus medialis muscles can be used as an accurate parameter for the diagnosis of patellofemoral pain syndrome (Ferrari et al., 2014). The 13–30 and 30–60 Hz bands have been associated with changes in voluntary effort (Neto et al., 2010) and the inability to activate the tibialis anterior muscle (30–60 Hz) with greater positional variability with the ankle joint in older adults when compared with young adults (Kwon et al., 2012).
The above studies suggest that frequency banding of the spectrum may prove a useful tool for detecting altered muscle function in patients with TMD. In studies of TMD patients, in the sEMG analysis in the frequency domain, the MDF is used to verify fatigue in the masseter and temporalis anterior muscles (Castroflorio et al., 2012, Pitta et al., 2015, Woźniak et al., 2015). Thus, examining the frequency characteristics of the sEMG signals from TMD patients could be a further step for providing a more complete description of the frequency characteristics of the masseter and anterior temporalis muscles, resulting in a better understanding of the impairments of these muscles, such as abnormal patterns of contraction, as described in previous studies (De Felício et al., 2013, De Felício et al., 2012, Lauriti et al., 2014, Tartaglia et al., 2011).
We tested the hypothesis that TMD patients present spectral change in specific frequency-bands in relation to healthy individuals and that this information could contribute to the development of frequency domain methods for identification and diagnosis of these dysfunctions.
The aim of this study was to verify whether any specific PSDF frequency bands of the sEMG signals are more susceptible to changes in TMD patients in comparison to healthy subjects.
Section snippets
Subjects
A convenience sample of 27 healthy adults and 27 TMD patients voluntarily participated in the experiment. After reading and providing informed consent, as approved by the University Ethics Committee (process n° 457625/2011), subjects completed a participant questionnaire that required information about the subjects and stomatognathic function abnormalities according to RDC/TMD (Research Diagnostic Criteria/Temporomandibular Disorder) (Schiffman et al., 2010). Each individual (patients and
Data analysis
The first five seconds of the raw signal acquired from MCE and CHW condition were analyzed. The analyzed parameters were calculated on the basis of the distribution of the data in a 1 s window from MCE and 5 s for the CHW condition.
The data were analyzed offline using specific routines carried out in the Matlab program (version R2010a; The MathWorks Inc., Natick, Massachusetts, USA).
Results
Table 1 presents the demographic data of healthy subjects and TMD patients and clinical pain information for the patients with TMD.
Discussion
To our knowledge, this is the first study to explore the possibility that specific frequency bands of sEMG signals collected from masticatory muscles (masseter and anterior temporalis muscles) could reveal specific differences between TMD patients and healthy subjects. However, differences not happened in specific bands, but in a range of frequency bands between 20 and 100 Hz.
The results of the present study led to the observation that each frequency component provided a specific contribution to
Conclusions
The present study demonstrated a significantly greater range of frequency bands between 20 and 100 Hz and a lower median frequency of the sEMG signal among healthy subjects, when compared to patients with TMD. These differences in the spectral statistics were similar to tests carried out for chewing and maximal clenching effort.
Conflict of interest
The authors declare that there are no conflicts of interest.
Acknowledgements
This study is supported by the Universidade Nove de Julho (UNINOVE, Brazil) and the Brazilian fostering agencies Fundação de Amparo a Pesquisa (FAPESP; process number 2013/13839-9).
Prof. Fabiano Politti, received a degree in Physiotherapy in 2000 and MSc and Ph.D in Anatomy from State University of Campinas (UNICAMP), Brazil. He is currently a Full Professor at the Nove de Julho University (UNINOVE) and a Researcher at the Neuromuscular Research Laboratory at the same University. His current research interests include the study of the neuromuscular control, postural control, muscle fatigue, clinical applications of surface electromyography, musculoskeletal dysfunction on
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Prof. Fabiano Politti, received a degree in Physiotherapy in 2000 and MSc and Ph.D in Anatomy from State University of Campinas (UNICAMP), Brazil. He is currently a Full Professor at the Nove de Julho University (UNINOVE) and a Researcher at the Neuromuscular Research Laboratory at the same University. His current research interests include the study of the neuromuscular control, postural control, muscle fatigue, clinical applications of surface electromyography, musculoskeletal dysfunction on area rehabilitation.
Claudia Casellato, obtained a Ph.D degree in Bioengineering in 2011 at the Politecnico di Milano; currently she is a post-doc fellow at NearLab (http://www.nearlab.polimi.it/). Her research activities are related to neurorobotics and neuromotor control mechanisms, with the development of robotic tools for neurorehabilitation and for biomimetic modeling.
Marcelo Martins Kalytczak, received his Master’s degree in Rehabilitation Sciences in 2015 from Nove de Julho University, Brazil. For his Master’s thesis, he investigated kinematic variables and electromyographic activity in patellofemoral pain syndrome patients at the Neuromuscular Research Laboratory of the Nove de Julho University, Brazil.
Marília Barbosa Santos Garcia, received her degree in Rehabilitation Sciences in 2015 from Nove de Julho University, Brazil. Her current research interest include the musculoskeletal dysfunction on area rehabilitation, temporomandibular disorders, questionnaries and measurement properties.
Prof. Daniela Aparecida Biasotto-Gonzalez - Received a degree in Physiotherapy in 1996 from University Medodista (UNIMEP) and MSc and Ph.D in Anatomy from State University of Campinas (UNICAMP), Brazil, in 2000 and 2002, respectively. Professor and researcher, Department of Rehabilitation Sciences at the University Nove de Julho (UNINOVE), São Paulo, Brazil. Extensive experience in the temporomandibular disorders, clinical applications of surface electromyography, posture, musculoskeletal dysfunction on area rehabilitation. Author of the book Interdisciplinary Approach to Temporomandibular Disorders - Manole Editor.