## 2 Background and Existing Work  
Blood pressure (BP) measurement techniques that rely on cuff inflations, such as the auscultatory method based on Korotkoff sounds, remain common in clinical settings. While these methods are useful in clinical environments, they come with several limitations, including inaccuracy, the inability to provide continuous BP measurements, and the incapacity to capture BP fluctuations over time, both during the day and during sleep. Additionally, they can be uncomfortable and may cause pain. The challenge of measuring BP during the night and while an individual is asleep has resulted in uncertainty surrounding various clinical issues. These issues include assessing the relationship between hypertension and obstructive sleep apnea and understanding the high frequency of strokes and heart attacks during sleep. In a study conducted by [PERSON] et al., impaired sleep quality and sleep deficiency were identified as contributors to hypertension, with positive correlations found between sleep deprivation and various adverse cardiovascular risk factors [19]. There is a growing demand for the development of methods that enable continuous, cuffless BP monitoring. Several approaches exist, but their widespread adoption is hindered by implementation difficulties. Intra-arterial, beat-by-beat BP recording is one technique that has been available for some time but has limited applicability due to its invasive nature in continuous monitoring. These methods are widely used in research. However, for everyday and common applications, they may not be the most convenient option due to their high cost and calibration difficulties. To address the calibration problem and improve blood pressure (BP) accuracy, a noninvasive and calibration-free BP estimation method has been developed for both young and older populations [20]. Mobile-based approaches relying on apps have also emerged. Over the last decade, the majority of these approaches have incorporated additional devices, such as wristbands, chest bands, and ECG sensors. There have been relatively few studies focusing on non-contact smartphone-based BP measurement [21]. Finally, there were pulse wave propagation approaches that were used to compute models to derive BP. Some of these models were based either on pulse transit time (PTT) measurement, in which a stretch-strain relationship is derived, or on pulse wave analysis methods to outline the existence of an inverse or non-inverse correlation between BP and PTT in a person due to the physical properties of arteries and can be obtained without a cuff [22]. They consider the use of PPG and ECG, which can be acquired in a nonobtrusive way. The wave that propagates through the tissues is highly dependent on the relationship between transmural pressure and the mechanical properties of the arterial vessel wall. These are critical properties of the human circulatory system, which is pressurized, whereby the wall's stiffness majority influences the pressure inside the vessel. Hence, the velocity of a traveling pulsed wave can serve as an indicator of arterial blood pressure (BP). This fundamental principle underlies the measurement of pulse wave velocity (PWV) to estimate arterial pressure. Pulse transit time (PTT), the time interval required for an electrical pulse to traverse between peripheral points, serves as an indicator of BP variation [23]. Researchers can use numerous approaches according to different wave properties from signals, especially heuristic modeling with regression [24], which generally uses machine learning algorithms for modeling. A novel clustering-based algorithm has been used recently to determine SBP and DPB from ABP by extracting only four features from ECG and PPG. They applied gradient boosting regression (GBR), random forest regression (RFR), and multilayer perceptron regression (MLP) on each cluster of MIMIC II data [25]. In the review literature [26], the authors provide a recent comprehensive advancement for non-invasive cuff-less BP using the PPG or ECG.