# Towards an Automatic Recognition of Artifacts and Features in Plethysmographic Traces
## 1 Introduction  
The determination of the venous outflow from the brain serves as an important metric in the diagnosis of cardiovascular diseases [1]. For instance, brain damage in a patient with diagnosed heart failure is investigated through the jugular venous pulse (JVP) waveform [2], a quantity related to the venous outflow within the internal jugular veins (IJVs) [2].  
Currently, the most common method for conducting this assessment entails the utilization of ultrasound technology performed by a qualified operator [2]. However, this approach presents a notable challenge in terms of automation and does not provide the possibility to perform continuous monitoring. An alternative method for the continuous monitoring of the JVP is central venous line catheterization, but this approach requires medical procedures, sometimes including surgery.  
Plethysmography is a non-invasive technique that measures the variation in the volume of a certain body district, typically in the wrist, the ankle, or the neck [3, 4, 5]. A plethysmograph provides complementary information with respect to other non-invasive techniques (e.g., ECG); however, its signal is more complex to analyze because of the dependence on the position and the biological variables of each patient [6, 7, 8]. Since the IJVs are the main vessels for blood return from the brain to the heart and travel through the neck right under the skin, they have been proven to be one of the most advantageous sites for the use of a plethysmograph [4].  
The IJVs are collapsible vessels whose cross-sectional area (CSA) depends on the transmural pressure on the vessel wall [9]. The pressure is also influenced by the position of the patient: In an upright position, the IJVs collapse, significantly reducing their CSA. When the patient is supine, the IJVs are the vessels with the largest blood outflow from the brain [6]. The variation in the venous outflow during a cardiac cycle yields a measurable variation in the IJVs' CSA, which can be detected through a small variation in the volume of the neck [10].  
The JVP pattern consists of three ascents or positive waves (\(a\), \(c\), and \(v\)) and three ascents or negative waves (\(x\), \(x^{\prime}\), and \(y\)) [1]:  
\begin{tabular}{l l} \(a\) & : originates from active atrial contraction leading to retrograde blood flow into neck veins. \\ \(x\) & : caused by continued atrial relaxation. \\ \(c\) & : due to the impact of the carotid artery adjacent to the jugular vein and retrograde transmission of a positive wave in the right atrium, produced by the right ventricular systole and the bulging of the tricuspid valve into the right atrium. \\ \(x^{\prime}\) & : caused by the descent of the right atrium floor (tricuspid valve) during right ventricular systole and continued atrial relaxation. \\ \(v\) & : corresponds to the maximal atrial filling; it is less prominent than the \(a\) ascent wave. \\ \(y\) & : follows the \(v\) wave and corresponds to the emptying of the atrium. \\ \end{tabular}  
These features are schematically shown in Figure 1.  
A plethysmographic trace exhibits significant variability in its features, largely influenced by the individual characteristics of each patient and their position during measurement (e.g., supine or sitting) [10]. As a result, the data typically require visual inspection and analysis. Artifacts caused by patient movements must be manually identified and removed from the records, while any visible waves require manual annotation by an expert.  
In this work, we demonstrate that the application of supervised machine learning techniques, and specifically neural networks, can significantly simplify this task by automatically recognizing artifacts and by identifying the most easily recognizable waves with a reasonable accuracy and specificity.  
Figure 1: The main features of the jugular venous pulse compared to an ECG trace.