## 1 Introduction  
Sleep apnea is a highly prevalent sleep disorder that can cause significant daytime sleepiness and result in many cardiovascular comorbidities [1; 2; 3; 4]. It is characterized by repetitive significant airflow reductions during sleep causing recurrent hypoxia and sleep fragmentation [5; 6; 7]. When breathing doesn't completely stop but the volume of air going into the lungs is significantly reduced, then the respiratory event is called a hypopnea. More than 200 million patients worldwide are affected with sleep apnea [8]. Sleep apnea events are three types: Obstructive, central, and mixed [9]. Obstructive sleep apnea (OSA) is characterized by repetitive upper airway obstruction that limit airflow from going in to the lungs with the presence of continued respiratory effort. Central sleep apnea (CSA) is characterized by the loss of all respiratory effort during sleep due to a neurological disorder. Mixed sleep apnea (MSA) is combination of both obstructive and central sleep apnea symptoms. Polysomnography (PSG), often called a sleep study, is the gold standard for detecting sleep apnea. Polysomnography records basic human body activities during sleep in an attended setting (sleep laboratory). This includes electrocardiogram (ECG) for heart, oronasal thermal airflow signal (FlowTh) and nasal pressure signal (NPRE) for respiration, electroencephalogram (EEG) for brain, electromyogram (EMG) for muscles, and oxygen level in the blood (SpO\({}_{2}\)) [10; 11]. Connecting a large number of sensors and wires to a subject in a dedicated sleep lab makes PSG uncomfortable, expensive, and unavailable to a large number of sleep patients in many parts of the world [9]. Moreover, clinicians need an offline inspection of the recordings to score apnea and derive the apnea-hypopnea index (AHI), which is the parameter used to establish sleep apnea and its severity [12]. Thus, the analysis process is labor-intensive and time-consuming, leading to a delayed diagnostic process and increased patient waiting lists [13; 14; 15; 16] as well as being highly susceptible to human errors [17]. To overcome limitations of PSG, several studies have been proposed for automated detection of sleep apnea using a limited subset of signals among those involved in PSG [18]. This includes respiratory signals [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29], ECG [16; 30; 31; 32], SpO\({}_{2}\)[33; 34; 35], tracheal sound signals [36], or some combinations of signals listed above [37]. A number of portable devices for sleep apnea monitoring and diagnosis have been developed and are available. LifeShirt, SleepStrip, and ApneaLink are among the most popular products [38]. Respiratory airflow signal is a straightforward choice to look for simpler alternatives to PSG, since apneas are primarily defined on the basis of its amplitude oscillations [12]. According to the American Academy of Sleep Medicine (AASM), the primary sensor for identifying apnea in sleep diagnostic studies is the oronasal thermal airflow sensor [11]. Thus, several studies focused on automated detection of sleep apnea events based exclusively on the analysis of this signal. In these studies, the airflow signal is analyzed in different analytic domains (linear, nonlinear, time and frequency domains) to extract features which are then used in a rule-based threshold classifier or in a \"black box\" machine learning model. Rule-based threshold classification has been used in [24; 39; 40; 41; 42]. Support Vector Machines (SVM) [43; 44], Artificial Neural Networks (ANN) [20; 45; 46; 47], linear discriminant analysis (LDA), and regression trees (CART) with the AdaBoost (AB) [22] are among the most popular machine learning models that used respiratory flow signal.