## 2 Related Work  
Recent advancements in sleep medicine and machine learning (ML) have significantly transformed the diagnosis and management of Obstructive Sleep Apnea (OSA). While polysomnography (PSG) remains the gold standard for diagnosing OSA, it is resource-intensive and inconvenient for patients. As a result, alternative approaches leveraging ML techniques have been developed to enhance diagnostic accuracy, improve patient experience, and reduce costs. Machine learning frameworks have demonstrated substantial promise in automating OSA diagnosis. For instance, [PERSON] et al. [8] proposed an ensemble-learning regression model to estimate OSA severity using at-home oximetry data. Their results showed that ML could provide accurate diagnostic outcomes while improving patient comfort. Similarly, [PERSON] et al. [9] employed decision tree models to evaluate key physiological traits derived from PSG data, enabling personalized therapy strategies for OSA. This personalized approach aligns with the need for tailored treatments, particularly in complex cases. Deep learning models have been at the forefront of this research. [PERSON] et al. [10] employed convolutional neural networks (CNNs) to diagnose OSA in pediatric populations, reducing reliance on expensive PSG procedures. [PERSON] et al. [11] demonstrated the effectiveness of recurrent neural networks (RNNs) for processing single-channel respiration signals, highlighting their ability to extract temporal dependencies critical for apnea detection. Similarly, [PERSON] et al. [12] utilized a one-dimensional CNN to process single-lead ECG data, achieving high accuracy in distinguishing apnea from non-apnea events. Single-channel oximetry has emerged as a valuable diagnostic tool. [PERSON] et al. [13] demonstrated the feasibility of using single-channel oximetry for the mass screening of OSA, offering a low-cost, accessible solution. Random forest algorithms, as shown by [PERSON] et al. [14], enhance computational efficiency by selecting critical features, enabling real-time clinical applications. The integration of multiple physiological signals has further improved diagnostic accuracy. [PERSON] et al. [15] combined blood oxygen saturation (SpO2) and pulse rate data, utilizing deep learning models to detect apnea events with enhanced precision. [PERSON] et al. [16] explored advanced architectures such as Xception and residual networks, demonstrating significant improvements in classification performance. Non-invasive and wearable technologies have also gained traction. [PERSON] et al. [17] reviewed smartphone-based applications, highlighting their potential in correlating the apnea-hypopnea index (AHI) with oxygen desaturation levels, enabling early detection in home settings. Similarly, [PERSON] et al. [18] emphasized the role of wearable devices in monitoring sleep patterns and cardiovascular health, showcasing their real-time diagnostic capabilities. Probabilistic models have proven effective for respiratory event detection. [PERSON] et al. [19] utilized layered Hidden Markov Models (HMMs) to analyze PSG data, while [PERSON] and [PERSON] [20] employed dynamic Bayesian networks to identify obstructive apnea episodes from ECG-based time-series data. Upper Airway Stimulation (UAS) has emerged as a promising treatment for patients intolerant to Continuous Positive Airway Pressure (CPAP) therapy. [PERSON] et al. [21] demonstrated its long-term effectiveness in reducing respiratory events and improving patient adherence. Positional therapies, oral devices, hypoglossal nerve stimulation, and my-ofunctional therapy, by enhancing the function and coordination of oral and facial muscles, further diversify treatment options for conditions such as sleep apnea, orthodontic concerns, and speech disorders, as highlighted by [PERSON] et al. [22]. Research into home-based solutions has expanded diagnostic accessibility. [PERSON] et al. [23] reviewed advancements in home monitoring devices, emphasizing the role of ML algorithms in enhancing their accuracy and reliability.