# Towards an Automatic Recognition of Artifacts and Features in Plethysmographic Traces
## 6 Conclusions  
In this study, we demonstrated the feasibility of automatically recognizing artifacts and key features in the jugular venous pulse (JVP) waveform, significantly reducing the manual workload required for inspection and labeling. A model, based on LSTM, was developed to isolate trace segments containing artifacts, i.e., variations in the shape that do not come from the variation in the cross-sectional area of the internal jugular veins. This model enables the automated validation of data quality and exclusion of regions affected by artifacts, eliminating the need for manual analysis.  
Additionally, a dedicated convolutional neural network (CNN) was designed to identify the most clinically relevant features in the waveform, specifically the \(c\) and \(y\) peaks. An efficiency of \(0.94^{+0.06}_{-0.18}\) and \(0.85^{+0.15}_{-0.17}\) and a purity of \(0.92^{+0.08}_{-0.17}\) and \(0.89^{+0.11}_{-0.16}\) could be achieved for the \(c\) and \(y\) peaks, respectively. The analyses were conducted using only the plethysmographic signal, without incorporating concurrent signals like the ECG signal.  
These results highlight the potential for expanding the approach. With a larger, more diverse dataset and higher sampling frequencies, the models could be extended to automatically recognize additional features and determine the posture of the patient (sitting or supine). Incorporating a more extensive and heterogeneous dataset, including patients with specific pathologies and individuals over 50 years old, would enable the model to be trained for the identification of pathological indicators across a wide spectrum of ages, sexes, and body positions, thereby enhancing its applicability and clinical relevance.  
Conceptualization, A.Z.; methodology, A.P., G.P. and A.Z.; data acquisition, A.P., G.P. and A.T.; data curation, A.P., G.P., A.T. and A.Z.; model development, A.B., M.C., R.L. and K.P.; validation, A.B., M.C., R.L., K.P. and A.Z.; writing--original draft preparation, A.B., M.C., R.L., K.P. and A.Z.; writing--review and editing, A.Z.; visualization, A.B., M.C., R.L., K.P. and A.Z.; supervision, A.Z. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the National Institute for Nuclear Physics (INFN) within the next_AIM (Artificial Intelligence in Medicine: next steps; [[https://www.pi.infn.it/aim](https://www.pi.infn.it/aim)]([https://www.pi.infn.it/aim](https://www.pi.infn.it/aim)), accessed on 4 December 2024) research project (INFN-CSN5) and by INFN within the EPISE research project (INFN-CSN5). The work was also supported with a research contract co-funded by the European Union--PON Research and Innovation 2014-2020 pursuant to Article 24, paragraph 3, letter (a) of Law No. 240 of 30 December 2010 as amended and Ministerial Decree No. 1062 of 10 August 2021; contract code: 09-I-48230-1.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request.  
The authors are grateful to the researchers at the Center for Exercise Science and Sports, University of Ferrara, for providing the data sample including the physiological measurements useful for obtaining the results reported in this article. The authors are thankful to [PERSON] and [PERSON] for the fruitful discussions. CloudVeneto [47] is acknowledged for the use of computing and storage facilities.  
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.