A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals
A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals
复制标题
一种利用生理信号检测磨牙症患者的新型混合机器学习分类
DOI：
10.3390/app10217410
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发表时间：
2020-11-01
影响因子：
2.7
通讯作者：
Lai, Dakun
中科院分区：
文献类型：
--
作者：
Bin Heyat, Md Belal;Akhtar, Faijan;Lai, Dakun
磨牙症是一种睡眠障碍，患者会咬紧牙关。使用传统方法进行磨牙症检测是耗时、麻烦且昂贵的。因此，自动检测这种疾病的工具将减轻医生的工作量，并为患者提供有价值的帮助。在本文中，我们针对这一目标，并设计了一种自动的方法来检测磨牙症的生理信号，使用一种新的混合分类。我们从数据收集开始。然后，我们进行了生理信号的分析和功率谱密度的估计。在此之后，我们设计了新的混合分类器，使磨牙症的检测基于这些数据。从脑电图通道（C4-A1）将受试者分类为“健康”或“磨牙症”，获得了92%的最大特异性和94%的准确性。此外，从心电图通道（ECG 1-ECG 2）中对睡眠阶段（如清醒（w）阶段和快速眼动（REM）阶段）进行分类的最大特异性为86%，准确性为95%。结合磨牙症分类和睡眠阶段分类从脑电图通道（C4-P4）获得了90%的最大特异性和97%的准确性。结果表明，利用脑电信号（C4-P4）可以实现更准确的磨牙症检测。目前的工作可以应用于家庭监控系统磨牙症检测。
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.