A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Temporomandibular Joint Disorders · Obstructive Sleep Apnea Research · Advanced Sensor and Energy Harvesting Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Robotics and Internet of Thing Lab, Prince Sultan University, Riyadh 11586, Saudi Arabia
Department of Information and Communication Engineering, Harbin Institute of Technology, Harbin 150001, China
SEICTLab, LR18ES44, Enicarthage, University of Carthage, Tunis 2035, Tunisia
Department of Orthodontics and Dentofacial Orthopedics, ZA Dental College and Hospital, Aligarh Muslim University, Aligarh 202002, India
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Funding
Prince Sultan University
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