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Article

A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals

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School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
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School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
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Robotics and Internet of Thing Lab, Prince Sultan University, Riyadh 11586, Saudi Arabia
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Department of Information and Communication Engineering, Harbin Institute of Technology, Harbin 150001, China
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SEICTLab, LR18ES44, Enicarthage, University of Carthage, Tunis 2035, Tunisia
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Department of Orthodontics and Dentofacial Orthopedics, ZA Dental College and Hospital, Aligarh Muslim University, Aligarh 202002, India
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School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(21), 7410; https://doi.org/10.3390/app10217410
Received: 13 September 2020 / Revised: 15 October 2020 / Accepted: 20 October 2020 / Published: 22 October 2020
Bruxism is a sleep disorder in which the patient clinches and gnashes their teeth. Bruxism detection using traditional methods is time-consuming, cumbersome, and expensive. Therefore, an automatic tool to detect this disorder will alleviate the doctor workload and give valuable help to patients. In this paper, we targeted this goal and designed an automatic method to detect bruxism from the physiological signals using a novel hybrid classifier. We began with data collection. Then, we performed the analysis of the physiological signals and the estimation of the power spectral density. After that, we designed the novel hybrid classifier to enable the detection of bruxism based on these data. The classification of the subjects into “healthy” or “bruxism” from the electroencephalogram channel (C4-A1) obtained a maximum specificity of 92% and an accuracy of 94%. Besides, the classification of the sleep stages such as the wake (w) stage and rapid eye movement (REM) stage from the electrocardiogram channel (ECG1-ECG2) obtained a maximum specificity of 86% and an accuracy of 95%. The combined bruxism classification and the sleep stages classification from the electroencephalogram channel (C4-P4) obtained a maximum specificity of 90% and an accuracy of 97%. The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4). The present work can be applied for home monitoring systems for bruxism detection. View Full-Text
Keywords: machine learning; hybrid classifier; sleep disorder; dental disorder; EEG; ECG; EMG machine learning; hybrid classifier; sleep disorder; dental disorder; EEG; ECG; EMG
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MDPI and ACS Style

Bin Heyat, M.B.; Akhtar, F.; Khan, A.; Noor, A.; Benjdira, B.; Qamar, Y.; Abbas, S.J.; Lai, D. A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals. Appl. Sci. 2020, 10, 7410. https://doi.org/10.3390/app10217410

AMA Style

Bin Heyat MB, Akhtar F, Khan A, Noor A, Benjdira B, Qamar Y, Abbas SJ, Lai D. A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals. Applied Sciences. 2020; 10(21):7410. https://doi.org/10.3390/app10217410

Chicago/Turabian Style

Bin Heyat, Md Belal, Faijan Akhtar, Asif Khan, Alam Noor, Bilel Benjdira, Yumna Qamar, Syed Jafar Abbas, and Dakun Lai. 2020. "A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals" Applied Sciences 10, no. 21: 7410. https://doi.org/10.3390/app10217410

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