{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:29:16Z","timestamp":1781281756528,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education (MOE)","award":["2023-RIS009"],"award-info":[{"award-number":["2023-RIS009"]}]},{"name":"Ministry of Education (MOE)","award":["NRF-2020H1D3A1A04081545"],"award-info":[{"award-number":["NRF-2020H1D3A1A04081545"]}]},{"name":"Ministry of Science and ICT","award":["2023-RIS009"],"award-info":[{"award-number":["2023-RIS009"]}]},{"name":"Ministry of Science and ICT","award":["NRF-2020H1D3A1A04081545"],"award-info":[{"award-number":["NRF-2020H1D3A1A04081545"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Diagnosis of bruxism is challenging because not all contractions of the masticatory muscles can be classified as bruxism. Conventional methods for sleep bruxism detection vary in effectiveness. Some provide objective data through EMG, ECG, or EEG; others, such as dental implants, are less accessible for daily practice. These methods have targeted the masseter as the key muscle for bruxism detection. However, it is important to consider that the temporalis muscle is also active during bruxism among masticatory muscles. Moreover, studies have predominantly examined sleep bruxism in the supine position, but other anatomical positions are also associated with sleep. In this research, we have collected EMG data to detect the maximum voluntary contraction of the temporalis and masseter muscles in three primary anatomical positions associated with sleep, i.e., supine and left and right lateral recumbent positions. A total of 10 time domain features were extracted, and six machine learning classifiers were compared, with random forest outperforming others. The models achieved better accuracies in the detection of sleep bruxism with the temporalis muscle. An accuracy of 93.33% was specifically found for the left lateral recumbent position among the specified anatomical positions. These results indicate a promising direction of machine learning in clinical applications, facilitating enhanced diagnosis and management of sleep bruxism.<\/jats:p>","DOI":"10.3390\/s24165426","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T12:58:07Z","timestamp":1724417887000},"page":"5426","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Advanced Sensing System for Sleep Bruxism across Multiple Postures via EMG and Machine Learning"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1230-2546","authenticated-orcid":false,"given":"Jahan Zeb","family":"Gul","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Maynooth University, W23A3HY Maynooth, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noor","family":"Fatima","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, AIR University, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5756-7284","authenticated-orcid":false,"given":"Zia","family":"Mohy Ud Din","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, AIR University, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8716-2907","authenticated-orcid":false,"given":"Maryam","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Faculty of Applied Energy System, Jeju National University, Jeju 63243, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2747-6618","authenticated-orcid":false,"given":"Woo Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Faculty of Applied Energy System, Jeju National University, Jeju 63243, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4712-6404","authenticated-orcid":false,"given":"Muhammad Muqeet","family":"Rehman","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Faculty of Applied Energy System, Jeju National University, Jeju 63243, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e180002","DOI":"10.1590\/1980-549720180002","article-title":"Prevalence and associated factors to sleep bruxism in adolescents from Teresina, Piau\u00ed","volume":"21","author":"Sousa","year":"2018","journal-title":"Rev. Bras. Epidemiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1038\/sj.bdj.2018.757","article-title":"Sleep bruxism: An overview for clinicians","volume":"225","author":"Beddis","year":"2018","journal-title":"Br. Dent. J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Walczy\u0144ska-Dragon, K., Kurek-G\u00f3recka, A., Niemczyk, W., Nowak, Z., Baron, S., Olczyk, P., Nitecka-Buchta, A., and Kempa, W.M. (2024). Cannabidiol Intervention for Muscular Tension, Pain, and Sleep Bruxism Intensity\u2014A Ran-domized, Double-Blind Clinical Trial. J. Clin. Med., 13.","DOI":"10.3390\/jcm13051417"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Buzatu, R., Luca, M.M., Castiglione, L., and Sinescu, C. (2024). Sinescu, Efficacy and Safety of Botulinum Toxin in the Management of Tem-poromandibular Symptoms Associated with Sleep Bruxism: A Systematic Review. Dent. J., 12.","DOI":"10.3390\/dj12060156"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bulanda, S., Ilczuk-Rypu\u0142a, D., Nitecka-Buchta, A., Nowak, Z., Baron, S., and Postek-Stefa\u0144ska, L. (2021). Sleep bruxism in children: Etiology, diagnosis and treatment\u2014A literature review. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18189544"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Martynowicz, H., Dymczyk, P., Dominiak, M., Kazubowska, K., Skomro, R., Poreba, R., Gac, P., Wojakowska, A., Mazur, G., and Wieckiewicz, M. (2018). Evaluation of intensity of sleep Bruxism in arterial hypertension. J. Clin. Med., 7.","DOI":"10.3390\/jcm7100327"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rupavatharam, S., and Gruteser, M. (2019, January 9\u201310). Towards In-Ear Inertial Jaw Clenching Detection. Proceedings of the 1st International Workshop on Earable Computing, EarComp 2019, London, UK.","DOI":"10.1145\/3345615.3361134"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bondareva, E., Hauksdottir, E.R., and Mascolo, C. (2021). Earables for Detection of Bruxism: A Feasibility Study. UbiComp\/ISWC 2021\u2014Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, New York, NY, USA, 21\u201326 September 2021, Association for Computing Machinery, Inc.","DOI":"10.1145\/3460418.3479327"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kaufmann, S., Ardelt, G., Malhotra, A., and Ryschka, M. (2013). In-Ear Pulse Wave Measurements: A Pilot Study. Biomed. Eng. Biomed. Tech., 58.","DOI":"10.1515\/bmt-2013-4128"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"23402","DOI":"10.3390\/s150923402","article-title":"Wearable Sensing of in-Ear Pressure for Heart Rate Monitoring with a Piezoelectric Sensor","volume":"15","author":"Park","year":"2015","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/JSEN.2021.3128246","article-title":"An Intraoral Non-Occlusal MEMS Sensor for Bruxism Detection","volume":"22","author":"Cogan","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Saczuk, K., Lapinska, B., Wilmont, P., Pawlak, L., and Lukomska-Szymanska, M. (2019). The bruxoff device as a screening method for sleep bruxism in dental practice. J. Clin. Med., 8.","DOI":"10.3390\/jcm8070930"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kusche, R., Klimach, P., Malhotra, A., Kaufmann, S., and Ryschka, M. (2015). An in-ear pulse wave velocity measurement system using heart sounds as time reference. Current Directions in Biomedical Engineering, Walter de Gruyter GmbH.","DOI":"10.1515\/cdbme-2015-0090"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0267-6605(92)90048-X","article-title":"A piezoelectric film transducer for dental occlusal analysis","volume":"10","author":"Sakaguchi","year":"1992","journal-title":"Clin. Mater."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gu, Y., Bai, Y., and Xie, X. (2021). Bite Force Transducers and Measurement Devices. Front. Bioeng. Biotechnol., 9.","DOI":"10.3389\/fbioe.2021.665081"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1067\/mpr.2001.115487","article-title":"A piezoelectric film-based intrasplint detection method for bruxism","volume":"86","author":"Takeuchi","year":"2001","journal-title":"J. Prosthet. Dent."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gao, J., Su, Z., and Liu, L. (2023). Design and Implement Strategy of Wireless Bite Force Device. Bioengineering, 10.","DOI":"10.3390\/bioengineering10050507"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1111\/joor.12223","article-title":"A sleep bruxism detection system based on sensors in a splint\u2014Pilot clinical data","volume":"42","author":"Mcauliffe","year":"2015","journal-title":"J. Oral. Rehabil."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Macedo, C.R., Silva, A.B., Machado, M.A., Saconato, H., and Prado, G.F. (2007). Occlusal Splints for Treating Sleep Bruxism (Tooth Grinding), John Wiley and Sons Ltd.","DOI":"10.1002\/14651858.CD005514.pub2"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1109\/JLT.2019.2922616","article-title":"Case Study for Monitoring the Rhythmic Masticatory Muscle Activity During Sleep Bruxism Episodes by Using Fiber Bragg Gratings","volume":"37","author":"Fiorin","year":"2019","journal-title":"J. Lightwave Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jirakittayakorn, N., and Wongsawat, Y. (2014, January 26\u201328). An EMG instrument designed for bruxism detection on masseter muscle. Proceedings of the 7th 2014 Biomedical Engineering International Conference, Fukuoka, Japan. Available online: https:\/\/api.semanticscholar.org\/CorpusID:35889348.","DOI":"10.1109\/BMEiCON.2014.7017403"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Peruzzi, G., Galli, A., and Pozzebon, A. (2022, January 18\u201320). A Novel Methodology to Remotely and Early Diagnose Sleep Bruxism by Leveraging on Audio Signals and Embedded Machine Learning. Proceedings of the 2022 IEEE International Symposium on Measurements & Networking (M&N), Padua, Italy.","DOI":"10.1109\/MN55117.2022.9887782"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"11","DOI":"10.5755\/j02.eie.28838","article-title":"Detection of EMG signals by neural networks using autoregression and wavelet entropy for bruxism diagnosis","volume":"27","author":"Sonmezocak","year":"2021","journal-title":"Elektron. Elektrotechnika"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Heyat, M.B.B., Lai, D., Akhtar, F., Hayat, M.A.B., Azad, S., Azad, S., and Bruxism, S.A. (2020). Bruxism Detection Using Single-Channel C4-A1 on Human Sleep S2 Stage Recording. Intelligent Data Analysis: From Data Gathering to Data Comprehension, Wiley.","DOI":"10.1002\/9781119544487.ch17"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40814-020-00634-w","article-title":"Guidance for conducting feasibility and pilot studies for implementation trials","volume":"6","author":"Pearson","year":"2020","journal-title":"Pilot Feasibility Stud."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, C., Verma, A.K., Guragain, B., Xiong, X., and Liu, C. (2024). Classification of bruxism based on time-frequency and nonlinear features of single channel EEG. BMC Oral Health, 24.","DOI":"10.1186\/s12903-024-03865-y"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/JBHI.2013.2274532","article-title":"Use of electromyographic and electrocardiographic signals to detect sleep bruxism episodes in a natural environment","volume":"17","author":"Castroflorio","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Maoddi, P., Bianco, E., Letizia, M., Pollis, M., Manfredini, D., and Maddalone, M. (2022). Correlation between a Force-Sensing Oral Appliance and Electromyography in the Detection of Tooth Contact Bruxism Events. J. Clin. Med., 11.","DOI":"10.3390\/jcm11195532"},{"key":"ref_29","unstructured":"(2024, May 21). Muscles of Mastication\u2014Physiopedia. Available online: https:\/\/www.physio-pedia.com\/Muscles_of_Mastication."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Heyat, B.B., Akhtar, F., Khan, A., Noor, A., Benjdira, B., Qamar, Y., Abbas, S.J., and Lai, D. (2020). A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals. Appl. Sci., 10.","DOI":"10.3390\/app10217410"},{"key":"ref_31","unstructured":"B. S. Inc. (2024, February 22). \u201cMP36-MP45\u201d. Available online: https:\/\/www.biopac.com\/wp-content\/uploads\/MP_Hardware_Guide.pdf."},{"key":"ref_32","first-page":"4956","article-title":"Correlation Between Bruxism and Facial Movement Coordination","volume":"20","author":"Salah","year":"2022","journal-title":"NeuroQuantology"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pandit, M., Gaur, M.K., and Kumar, S. (2023). Frequency, and Wavelet Transform Methods. Artificial Intelligence and Sustainable Computing, Springer Nature.","DOI":"10.1007\/978-981-99-1431-9"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5426\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:41:00Z","timestamp":1760110860000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"references-count":33,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24165426"],"URL":"https:\/\/doi.org\/10.3390\/s24165426","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}