## 1 Introduction  
The present technological trend of digitalization brings multiple advantages across various domains where human activity is developing. One important domain is the medical environment, where a new wide range of smart devices [1] has risen to aim at self-monitoring. This shift in paradigm is given by the increase in popularity of wearable devices which are capable of tracking cardiovascular markers. The most common ones are represented by heart rate (HR) and blood oxygen saturation level (SpO2), followed by blood pressure (BP) evaluation and even an electrocardiogram (ECG) [2, 3]. A major contributor to bring such monitoring features closer to user hand is represented by photoplethysmography (PPG) techniques which gains interest and popularity in the last decade across scientific circles. The obtained electrical signal by illuminating capillaries contains an abundance of cardiovascular information which waits to be interpreted. Except for the previous presented parameters, this technique shows a good confidence level to detect changes in vasomotor and respiratory activities. It was demonstrated that vasodilators or vasomotor drugs modulate the morphology of PPG signals especially by the dicrotic wave behavior [4; 5; 6]. Additionally, the respiratory activity plays a major role since it modulates the signal where a connection was found between modulation amplitude and hypertensive patients [7]. Therefore, based on this vasomotor activity, the trend of BP value can be extracted since it is one of the critical health markers [8]. Therefore, multiple studies in this direction started and some of them offer the reference to the used public databases such as China Database, MIMIC III, CapnoBase, and University of Queensland Database [9; 10; 11; 12]. Although the search to extract BP prediction from analysing PPG waveform is approached from multiple directions, existing studies do not take into consideration a solid model which connects the mechanical nature of blood pressure with the optical nature of photoplethysmography. Despite those mechanisms being still in debate, especially related to the true origin of PPG and its morphology, it did not represent a blocking point to assess cardiovascular characterization at some confidence level [13; 14; 15]. For example, in a cold environment, vasoconstriction on peripheral area is triggered, thus the amplitude of PPG signal decreases [16]. Another factor is detecting certain cardiovascular disorders such as pulsus bisferiens [17] or pulsus alternans which are detected within waveform analysis without knowing exactly the above-depicted models. In this way, without focusing on the origin topic, we started to define at a first glance, the desired level of cardiovascular information which can be extracted from the optical signal. We identify three levels of prediction, depicted in Figure 1, as follows:  
* Level 1--PPG signal can predict BP class (hypotensive, normotensive, hypertensive) in an individual case. This prediction can be scaled-up to a larger population and represent the first assessment in an advanced signal processing topic. * Level 2--PPG signal can predict BP trend qualitatively (rising or falling) during a certain time frame observation in an individual monitoring case. This level is proven by drug administration during anaesthesia procedure and even by the respiratory cycle where PPG waveform is modulated. This prediction can be also scaled-up to a larger population but involves a large time of signal observation unlike in the first level. * Level 3--PPG signal can predict BP value. This level predicts the BP trend from a qualitative mean. It is hard to achieve this prediction rate because a change of \(+/-15\) mmHg for example, brings up different waveform change in two different persons. Work-around techniques such as PTT which fusions a second sensor like ECG proves a good achievement at this step. This level also requires a calibration step since PPG signal gives to the processing system only optical quantities.