## 1 Introduction  
Sleep apnea is one of the most prevalent sleep disorders, causing significant daytime sleepiness and cardiovascular comorbidities [1; 2; 3; 4]. The repetitive airflow reductions, due to sleep apnea, can cause recurrent hypoxia and sleep fragmentation [5; 6; 7]. In the United States, more than 200 million patients are known to suffer from sleep apnea [8]. Sleep apnea can be divided into three major categories: obstructive, central, and mixed apnea [9]. Among them, obstructive sleep apnea (OSA) is the most common one; it is described as an occasional pause in breathing during sleep, caused by the collapse or obstruction of the upper airway. OSA causes loud snoring or choking noises in patients due to the repetitive attempts to breathe with the repeated airway collapse for increasing the inspired amount of air. Thus, OSA obstructs the patient's brain and body from taking enough oxygen, which may cause patients to wake up with reduced sleep quality, also leading to degraded activities and excessive sleepiness during the daytime [10]. Polysomnography (PSG) is often regarded as the gold standard for detecting sleep apnea. It monitors physiological signals while sleeping in a dedicated laboratory, includingelectrocardiogram (ECG), oronasal thermal airflow signal (FlowTh), nasal pressure signal (NPRE), electroencephalogram (EEG), electromyogram (EMG), and oxygen levels in the blood (SpO\({}_{2}\)) [11; 12; 13]. This laboratory environment for PSG makes participants uncomfortable, due to the number of sensors and wires and costly examination fees. PSG is more expensive because of an offline inspection of clinicians for deriving the apnea-hypopnea index (AHI) based on visual scoring by an expert sleep clinician [14]. The AHI is used as a parameter for evaluating sleep apnea and its severity. Consequently, the process of PSG is time-demanding and labor-intensive [15; 16; 17; 18] and is vulnerable to human errors [19]. Many screening tools have been developed to identify patients at high risk for OSA, including the Berlin questionnaire (BQ), NoSAS, NAMES, No-Apnea, and STOP-BANG. The Berlin questionnaire (BQ) was developed in 1999 by [PERSON] et al. [20], and now it is one of the most popular questionnaires to screen OSA. Patients can be classified into having a high risk or low risk of OSA based on their overall responses to the 10 questions (e.g., Do you snore? How often do you snore? etc.). The NoSAS score was introduced by [PERSON] et al. [21] to identify individuals at risk of sleep-disordered breathing; this screening tool consists of five significant factors to diagnose sleep-disordered breathing, which includes neck circumference, obesity, snoring, age, and sex. In addition, [PERSON] et al. [22] introduced the NAMES assessment (neck circumference, airway classification, comorbidities, Epworth scale, and snoring) for the detection of OSA; this screening tool was developed by combining both self-reported historical factors with physical exam findings of OSA. [PERSON] et al. [23] developed a practical model, the No-Apnea screening tool, which consists of only two variables (neck circumference and age). The range of the total score is from 0 to 9 points, and they used a cutoff of \(\geq\)3 to classify patients at a high risk of having OSA. [PERSON] et al. [24] introduced the STOP-BANG questionnaire to evaluate OSA. The questionnaire includes four yes or no questions about snoring (S), tiredness during daytime (T), observed apnea (O), and high blood pressure (P). These four questionnaires have been well adopted in the medical field to evaluate the severity of OSA, and all four of them are actively used. The previously developed screening tools were validated and showed marginal accuracy in detecting OSA. For example, [PERSON] et al. [24] validated the STOP questionnaire based on the AHI from PSG. The sensitivities of the STOP questionnaire with three AHI cutoffs (AHI \(>\) 5, \(>\)15, and \(>\)30) were determined as 65.6%, 74.3%, and 79.5%, respectively.