## 2 Literature Review  
There are several methods have been proposed to analyze the physiological signals to detect OSA. Most methods try to find a breathing pattern, ECG, SaO2, and nasal airflow collected from humans using several sensors [16; 17]. In general, detecting sleep apnea is \begin{table}
\begin{tabular}{c c} \hline \hline
**Wave Name** & **Description** \\ \hline P & wave is the contraction pulse of the atrial systole. \\ Q & wave is a descendant deflection that followed directly the P wave. \\ R & wave illustrates the ventricular contraction. \\ S & wave is the down deflection immediately after the R wave. \\ T & wave represents the ventricular recovery. \\ U & wave succeeds the T wave but it is generally ignored, \\ P-R & is the time that the electrical impulse takes to travel from the sinus node through the AV node. \\ R-R & segment is the distance between two successive R peaks. \\ QRS & complex represents the ventricular contraction and depolarization. \\ S-T & segment is generally isoelectric and it begins after the QRS Complex. \\ Q-T & interval is the distance from the start of the QRS complex to the end of the T wave. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main components of ECG signal. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ECG Features** & **Duration (s)** & **Amplitude (mv)** \\ \hline P Wave & 0.08–0.1 & 0.25 \\ T Wave & 0.16–0.2 & \(>\)0 \\ QRS Complex & 0.08–0.1 & Q \(<\) 0, R \(>\) 0, S \(<\) 0 \\ R-R Interval & 0.6–1.2 & - \\ P-R Interval & 0.12–0.22 & R \(>\) 0 \\ S-T Interval & 0.2–0.32 & isoelectric \\ Q-T Interval & 0.35–0.45 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Wave names inside ECG signal. performed in the hospital with a sleep lab facility. Some home testing devices help patients to do sleep apnea tests at an affordable cost [16; 18]. The first paper published about the effects of sleep apnea on the human heart's electrical activity was in 1984 by [PERSON] et al. [19]. The authors report that the OSA has a high correlation with bradycardia during apnea time. The apnea usually occurs in 10-20 s, which affect the heartbeat [12]. In simple, apnea appears as a frequency component (i.e., the range is 0.05 Hz to 0.1 Hz) to the Respiration Rate (R.R.) interval tachogram related to the apnea duration. So, it is hard to determine the existence of apnea based on these additional frequency components. Many researchers start employing ML as an intelligent solution to detect the OSA based on heart rate to overcome this difficulty. Many research papers highlight the ability to employ ML in detecting sleep apnea. For example, [PERSON] and [PERSON] [20] used a combination of different ML classifiers (i.e., AdaBoost with Decision Stump, and Bagging with REPTree) to detect sleep apnea. Moreover, the authors applied feature selection as a preprocess for collected ECG signals. The obtained results show a good accuracy value equals 82%. [PERSON] et al. [21] investigate the performance of 60 ML (i.e., regression and classification algorithms) to predict the Apnea-Hypopnea Index (AHI). The authors conclude the importance of ML in detecting sleep apnea. [PERSON] et al. [22] proposed a simple graphical representation to detect OSA for adult patients. The proposed system can determine the existence of OSA based on a visual inspection of the RR-interval tachogram. [PERSON] et al. [23] examine 90 patients to investigate the occurrence of OSA. The authors applied three methods for extracting respiratory events from two types of ECG signals (i.e., single-lead and multi-lead). The obtained results show that the events from extracted multi-lead ECGs can improve the detection rate (i.e., sensitivity equals 85%, and specificity equals 89%. [PERSON] et al. [24] report that there is a high correlation between sleep apnea and cardiovascular disease. Many research papers used the single-lead ECG to detect the sleep apnea [25; 26; 27]. For example, [PERSON] et al. [28] proposed a novel automated method to detect sleep apnea based on single-lead ECG.