## 1 Introduction
Some systems such as electrocardiogram (ECG) (cardiac signal) [20; 21], electrooculogram (EOG) (eye signal), electroencephalogram (EEG) (brain signal) [22], and electromyogram (EMG) (muscles signal) [23] are used in automatic sleep stage detection [24]. [PERSON] et al. [25] evaluated the clinical practice on French dental clinicians. The authors used one thousand three hundred and eighty-eight practitioners based on five methods, such as oral rehabilitation, treatment of the patient with occlusal splints, sociodemographic characteristics, diagnosis, and management of sleep  
Figure 1: Comparison between (**A**) healthy and (**B**) bruxism teeth of the human. The grinding of the tooth is shown in the case of bruxism. bruxism. The work discovered 16.8% wide inequality and inadequate diagnosis, and 21.9% of applicants planned cognitive-behavioral treatment. [PERSON] et al. [26] evaluated the screening and detection method of bruxism. The researchers used the total number of sixty adults, including twenty-five healthy and thirty-five with bruxism. They evaluated the masseter muscle activity of the body. [PERSON] et al. [27] studied the validity of the detection of bruxism using EMG and a cut-off rate with optimum sympathy and specificity. A total number of 16 subjects were used in this work. The authors suggested that one-channel EMG and a cut-off value are appropriate for the detection of bruxism disorder. [PERSON] et al. [28] suggested that the ambulatory electrode is the highest-precision device for the diagnosis of bruxism sleep disorder and sleep stage scoring. [PERSON] et al. [29] represented the diagnosis of sleep bruxism using analysis and acquisition of the human masseter muscle by an EMG signal. [PERSON] et al. [30] investigated that remote communication and piezoelectric sensors are used in the diagnosis of bruxism sleep disorder. [PERSON] and [PERSON] [31] investigated that multi-source data with sympathovagal balance valuation are used for the early diagnosis of bruxism disorder stages. [PERSON] and [PERSON] [32] designed an EMG instrument for the detection of sleep bruxism patients on the masseter muscle. We propose a new detection system for bruxism using the extraction of power spectral density on physiological sleep recordings such as ECG (ECG1-ECG2), EMG (EMG1-EMG2), and EEG (C4-P4 and C4-A1). Initially, the physiological signal is extracted from the sleep database. The 10/20 standard sleep recording system recorded these data. It is used in the research work of sleep disorders such as circadian rhythm sleep disorders, insomnia [33; 34; 35; 36; 37], bruxism [38; 39; 40], sleep apnea, restless leg syndrome, narcolepsy [41], and nocturnal frontal lobe epilepsy [42]. We preprocessed the signals using a low-pass finite impulse response (FIR) filter to remove the noise of the signal. After filtering the signal, we estimated the power spectral density of the signals. Finally, we classified the sleep stages (w and REM) [43; 44], subjects (bruxism and healthy), and combined subjects (bruxism and healthy) and sleep stages (w and REM) from the novel hybrid machine learning (HML) classifier. The proposed hybrid classifier is the combination of ten machine learning classifiers, namely K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), naive Bayes (NB), linear regression (LR), classification and regression tree (CART), linear discriminant analysis (LDA), AdaBoost (AB), gradient boosting (GB), and extra trees (ET). There are two common issues in the previous research of bruxism. Firstly, there is a large number of features used in the studies, which results in a high computational load. Secondly, most of these methods did not represent a classification with sleep stages. We address these challenges in this study. Further, we focus on some goals, including estimation of the power spectral density, applying the novel HML classifier for the classification methods on the same feature, and comparison between the HML classifier and different previously used classifiers.