Classification of sEMG Signals for Diagnosis of Unilateral Posterior Crossbite in Primary Dentition using Fast Fourier Transform and Logistic Regression | ||
| Journal of AI and Data Mining | ||
| دوره 10، شماره 2، تیر 2022، صفحه 151-158 اصل مقاله (1.87 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2022.10908.2234 | ||
| نویسندگان | ||
| H. Kalani* 1؛ E. Abbasi2 | ||
| 1Department of Mechanical Engineering, Sadjad University, Mashhad, Iran. | ||
| 2Medical Faculty-Islamic Azad University of Mashhad, Mashhad, Iran. | ||
| چکیده | ||
| Posterior crossbite is a common malocclusion disorder in the primary dentition that strongly affects masticatory function. To the best of the author’s knowledge, for the first time, this article presents a reasonable and computationally efficient diagnostic system for detecting characteristics between children with and without unilateral posterior crossbite (UPCB) in the primary dentition from the surface electromyography (sEMG) activity of masticatory muscles. In this study, 40 children (4–6y) were selected and divided into UPCB (n = 20) and normal occlusion (NOccl; n = 20) groups. The preferred chewing side was determined using a visual spot-checking method. The chewing rate was determined as the average of two chewing cycles. The sEMG activity of the bilateral masticatory muscles was recorded during two 20-s gum-chewing sequences. The data of the subjects were diagnosed by the dentist. In this study, the fast Fourier transform (FFT) analysis was applied to sEMG signals recorded from subjects. The number of FFT coefficients had been selected by using Logistic Regression (LR) methodology. Then the ability of a multilayer perceptron artificial neural network (MLPANN) in the diagnosis of neuromuscular disorders in investigated. To find the best neuron weights and structures for MLPANN, particle swarm optimization (PSO) was utilized. Results showed the proficiency of the suggested diagnostic system for the classification of EMG signals. The proposed method can be utilized in clinical applications for diagnoses of unilateral posterior crossbite. | ||
| کلیدواژهها | ||
| posterior crossbite؛ surface electromyography؛ multilayer perceptron artificial neural network (MLPANN)؛ particle swarm optimization (PSO) | ||
| مراجع | ||
|
[1] C. Maspero, L. Giannini, G. Galbiati, L. Kairyte and G. Farronato, G, “Neuromuscular evaluation in young patients with unilateral posterior crossbite before and after rapid maxillary expansion,” Stomatologija, vol.17 (3), pp. 84-88, 2015.
[2] B. Thilander, S. Wahlund, and B. Lennartsson, “The effect of early interceptive treatment in children with posterior crossbite,” Eur. J. Orthod., vol. 6(1), pp. 25-34, 1984.
[3] A. S. Andrade, G. H. Gameiro, M. DeRossi and M. B. D. “Posterior Crossbite and Functional Changes: A Systematic Review,” Angle Orthod., vol. 79(1), pp. 380-386, 2009.
[4] A. S. Pinto, P. H. Buschang, G. S. Throckmorton, P. Chen, “Morphological and positional asymmetries of young children with functional unilateral posterior cross-bite,” Am. J. Orthod. Dentofacial Orthop., vol 120(5), pp. 513-520, 2001.
[5] K. Woźniak, L. Szyszka-Sommerfeld, and D. Lichota, “The electrical activity of the temporal and masseter muscles in patients with TMD and unilateral posterior crossbite” Biomed Res. Int., 2015.
[6] M. Wieckiewicz, M. Zietek, D. Nowakowska, W. Wieckiewicz, “Comparison of selected kinematic facebows applied to mandibular tracing,” Biomed Res. Int., 2014.
[7] I. Veli, T. Uysal, T. Ozer, F. I. Ucar, M. Eruz, “Mandibular asymmetry in unilateral and bilateral posterior cross-bite patients using cone-beam computed tomography,” Angle Orthod., vol. 81(6), pp. 966-974, 2011.
[8] D. Braxton, C. Dauchel, and W. Brown, “Association between chewing efficiency and mastication patterns for meat, and influence on tenderness perception”, Food. Qual. Prefer., vol. 7, pp. 217-223, 1996.
[9] W. E. Brown, K. R. Langley, L. Mioche, S. Marie, S. Gérault, D. Braxton, “Individuality of understanding and assessment of sensory attributes of foods, in particular, tenderness of meat,” Food. Qual. Prefer., vol. 7, pp. 205-216, 1996.
[10] A. Monaco, F. Sgolastra, I. Ciarrocchi, R. Cattaneo, “Effects of transcutaneous electrical nervous stimulation on electromyographic and kinesiographic activity of patients with temporomandibular disorders: a placebo-controlled study,” J. Electromyogr. Kinesiol., vol. 22(3), pp. 463-468, 2012.
[11] A. D. S. Andrade, M. B. D. Gavião, G. H. Gameiro, M. D. Rossi, “Characteristics of masticatory muscles in children with unilateral posterior crossbite”, Braz. Oral Res., vol. 24, pp. 204-210, 2010.
[12] M. G. Piancino, D. Farina, F. Talpone, A. Merlo, and P. Bracco, P., “Muscular activation during reverse and non‐reverse chewing cycles in unilateral posterior crossbite”, Eur. J. Oral Sci, vol. 117, pp. 122-128, 2009.
[13] S. Tecco, S. Tetè, and F. Festa, “Electromyographic evaluation of masticatory, neck, and trunk muscle activity in patients with posterior cross-bites”, Eur. J. Orthod., vol. 32, pp. 747-752, 2010.
[14] S. G. Farias Gomes, W. Custodio, J. S. Moura Jufer, A. A. Del Bel Cury, R. C. M. Rodrigues Garcia, “Correlation of mastication and masticatory movements and effect of chewing side preference,” Braz. Dent. J., vol. 21, pp. 351-355, 2010.
[15] G. Iodice, G. Danzi, R. Cimino, S. Paduano, A. Michelotti, “ Association between posterior crossbite, skeletal, and muscle asymmetry: a systematic review,” Eur. J. Orthod., vol. 38, pp. 638-651, 2016.
[16] N. F. Güler, and S. Koçer, “ Classification of EMG signals using PCA and FFT” J. Med. Syst., vol. 29, pp. 241-250, 2005.
[17] S. Koçer, “Classification of EMG signals using neuro-fuzzy system and diagnosis of neuromuscular diseases,” J. Med. Syst., vol. 34, pp. 321-329, 2010.
[18] H. Kalani, , S. Moghimi, and A. Akbarzadeh, “Towards an SEMG-based tele-operated robot for masticatory rehabilitation,” Comput. Biol. Med., vol. 75, pp. 243-256, 2016.
[19] H. Kalani, , S. Moghimi, and A. Akbarzadeh, “Toward a bio-inspired rehabilitation aid: sEMG-CPG approach for online generation of jaw trajectories for a chewing robot,” Biomed. Signal Process. Control, vol. 51, pp. 285-295, 2019.
[20] N. Goharian, , S. Moghimi, and H. Kalani, “Application of an ANN-GA method for predicting the Biting force using electromyogram signals” Signal and Data Processing, vol. 14, pp. 41-52, 2017.
[21] M. Asefi, S. Moghimi, H. Kalani, A.Moghimi, “Dynamic modeling of SEMG–force relation in the presence of muscle fatigue during isometric contractions,” Biomed. Signal Process. Control, vol .28, pp. 41-49, 2016.
[22] h. Kalani, S. Moghimi, and A. Akbarzadeh, “SEMG-based prediction of masticatory kinematics in rhythmic clenching movements” Biomed. Signal Process. Control, vol 20, pp. 24-34, 2015.
[23] N. Goharian, S. Moghimi, and H. Kalani, “Estimation biting force based using EMG signals and Laguerre estimation technique, ” in Proceedings of 2015 23rd Iranian Conference on Electrical Engineering, Available: IEEE Xplore, http://www.ieee.org. [Accessed: 02 July 2015].
[24] H. Kalani, S. M. Tahamipour-Z, I. Kardan, A. Akbarzadeh, A. Ebrahimi, R. Sede, “SVM for Decoding the Human Activity Mode from sEMG Signals” in Proceedings of 2019 7th International Conference on Robotics and Mechatronics (ICRoM), Available: IEEE Xplore, http://www.ieee.org. [Accessed: 20 April 2020].
[25] N. Goharian, H. Kalani, and S. Moghimi, “A time-delay parallel cascade identification system for predicting jaw movements” in Proceedings of 2014 21st Iranian Conference on Biomedical Engineering (ICBME)I, Available: IEEE Xplore, http://www.ieee.org. [Accessed: 19 February 2015].
[26] H. Kalani, A. Akbarzadeh, and S. Moghimi, “Prediction of clenching jaw movements based on EMG signals using fast orthogonal search” in Proceedings of 2015 23rd Iranian Conference on Electrical Engineering, Available: IEEE Xplore, http://www.ieee.org. [Accessed: 02 July 2015].
[27] D. Barmpakos, P. Kaplanis, S. A. Karkanis, C. Pattichis, “Classification of neuromuscular disorders using features extracted in the wavelet domain of sEMG signals: a case study”, Health Technol., vol. 7, pp. 33-39, 2017.
[28] Wu, Qi, J. F. Mao, C. F. Wei, Shan Fu, Rob Law, Lu Ding, Bi-Ting Yu, B. Jia, and C. H. Yang., “Hybrid BF–PSO and fuzzy support vector machine for diagnosis of fatigue status using EMG signal features” Neurocomputing, vol. 173, pp. 483-500, 2016.
[29] A. Subasi, “Classification of EMG signals using PSO-optimized SVM for diagnosis of neuromuscular disorders” Comput. Biol. Med., vol. 43, pp. 576-586, 2013.
[30] V. Khoshdel; A. R Akbarzadeh, “ Application of statistical techniques and artificial neural network to estimate force from sEMG signals” J. AI Data Min. vol. 4, pp. 135-141, 2016.
[31] H. Kalani, M. Sardarabadi, and M. Passandideh-Fard, “Using artificial neural network models and particle swarm optimization for manner prediction of a photovoltaic thermal nanofluid based collector” Appl. Therm. Eng., vol. 113, pp. 1170-1177, 2017.
[32] A. S. Andrade, M. B. Gavião, M. Derossi, and G. H. Gameiro, “Electromyographic activity and thickness of masticatory muscles in children with unilateral posterior cross-bite,” Clinical Anatomy: The Official Journal of the American Association of Clinical Anatomists and the British Association of Clinical Anatomists, vol 22, pp. 200-206, 2009.
[33] N. Tsanidis, G. Antonarakis, and S. Kiliaridis, “Functional changes after early treatment of unilateral posterior cross‐bite associated with mandibular shift: a systematic review,” J. Oral Rehabil., vol. 43, pp. 59-68, 2016.
[34] B. Lucas, T. de S. Barbosa, L. J. Pereira, M. B. D. Gavião, and P. M. Castelo, “Electromyographic evaluation of masticatory muscles at rest and maximal intercuspal positions of the mandible in children with sleep bruxism,” Eur. Arch. Paediatr. Dent., vol. 15, pp. 269-274, 2014.
[35] E. M. A. Ibraheem, and M. Z. Nassani, “ The effect of flexible acrylic resin on masticatory muscle activity in implant-supported mandibular overdentures: a controlled clinical trial” Electronic physician., vol. 8, pp. 1752, 2016.
[36] K. H. L. Turcio, P. R. J. Zuim, A. M. Guiotti, D. M. Dos Santos, M. C. Goiato, D. A. Brandini, “Does the habitual mastication side impact jaw muscle activity?” Arch. Oral Biol., vol. 67, pp0 34-38, 2016.
[37] G. S. Throckmorton, P. H. Buschang, H. Hayasaki, A. S. Pinto, “Changes in the masticatory cycle following treatment of posterior unilateral crossbite in children” Am. J. Orthod. Dentofacial. Orthop., vol. 120, pp. 521-529, 2001. | ||
|
آمار تعداد مشاهده مقاله: 712 تعداد دریافت فایل اصل مقاله: 686 |
||