# Towards an Automatic Recognition of Artifacts and Features in Plethysmographic Traces
## 2 Related Works  
The present study aims to explore a non-invasive methodology for monitoring the jugular venous pulse signal. In recent years, numerous research groups have investigated the feasibility of using wearable devices to detect the characteristic peaks of the JVP waveform.  
Inertial sensors, such as accelerometers and gyroscopes, have been employed for this purpose due to their correlation with neck movements induced by venous pulse activity. [PERSON] et al. [11] used an accelerometer-based sensing system for the preliminary identification of the JVP signal by connecting the peaks of interest from the measured accelerometer trace. Similarly, [PERSON] et al. [12] achieved a reliable JVP waveform through the double integration of the accelerometric signal. In contrast, [PERSON] et al. [13] extracted the JVP signal by integrating the vector sum of angular velocity measurements obtained from a three-axis gyroscope. Additionally, a contact piezoelectric sensor was employed to measure the JVP signal, and the peak locations were determined using pulse contour markers [14].  
Another contact-based system consists of single-crystal ultrasound coupled to a foresensing load cell to measure the JVP based on the force required to collapse the IJV walls [15]. Furthermore, [PERSON] et al. [16] used a non-invasive optical sensing system to estimate the venous oxygen saturation of both the external and internal jugular veins, while [PERSON] and [PERSON] [17] proposed an anterior neck contact photoplethysmography system to extract information on the JVP.  
Regarding non-contact methodologies for JVP waveform analysis, researchers are validating the effectiveness of using microwave radar-based systems in both near- and far-field regions [18; 19; 20]. Custom photoplethysmography imaging systems, consisting of a near-infrared sensitive camera paired with an illumination source, have been used to quantify JVP waveforms based on skin displacements, revealing a strong negative correlation with arterial waveforms [21; 22]. Moreover, camera-based systems that acquire video recordings of neck skin movement are being developed to facilitate remote, non-contact JVP measurements for telehealth applications [23; 24].  
To date, research has predominantly focused on applying artificial intelligence techniques to arterial signals, which are easily detected by photoplethysmography (PPG) [25; 26]. Arterial blood pressure can be measured by commercially available wearable devices, where machine learning frameworks are widely used to accurately identify characteristic points of the PPG waveform, not only for cardiovascular applications [27] but also for the classification and prediction of depressive behavior [28], among others [29].  
In contrast, our research is dedicated to the automated analysis of the jugular venous pulse instead of the arterial pulse with machine learning techniques, which represents a research topic that is currently largely unexplored. Conversely to the studies presented in refs. [11; 13; 17; 19], the method presented in this article removes artifacts and identifies JVP peaks of interest in an automatic manner, while most studies on JVP signal analysis so far manually annotate the peaks of the main waves for each cardiac cycle [30].  
In this work, the JVP signal is acquired via an easy-to-use, wearable device that enables patients to autonomously monitor their health in an uncontrolled environment, such as their own home. Therefore, it is crucial to implement an algorithm capable of automatically eliminating signal artifacts and providing the quasi-real-time identification of JVP peaks, such as \(c\) and \(y\), which correspond to the bulging of the tricuspid valve into the right atrium and the rapid filling phase of the right ventricle after the tricuspid valve opens, respectively [31]. These are useful for the assessment of blood pulsation on the brain-heart axis. Future developments will aim to extend the number and the accuracy of features recognized.