@inproceedings{b50752f398ef4f15a32bcdede3009533,
title = "PPG-based cf-PWV Estimation Using Visibility Graph Image Representation and Transfer Learning",
abstract = "Carotid-to-femoral pulse wave velocity (cf-PWV) is a crucial biomarker, essential for cardiovascular disease diagnosis and prediction. However, the standard measuring of cf-PWV is highly complex making it prone to errors and inaccuracies. In this paper, a deep learning model based on visibility graph representation obtained from the non-invasive easily measured photoplethysmogram (PPG) waveform is proposed. The obtained results illustrate the feasibility and robustness of visibility graph for image based data-driven cf-PWV estimation from non-invasive PPG signals.Clinical relevance: This project reaches a promising R2 equal to or higher than 0.89 for the estimation of the cf-PWV from PPG signals extracted from the Radial artery.",
author = "Vargas, {Juan M.} and Bahloul, {Mohamed A.} and Laleg-Kirati, {Taous Meriem}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023 ; Conference date: 07-12-2023 Through 09-12-2023",
year = "2023",
doi = "10.1109/IEEECONF58974.2023.10405056",
language = "English (US)",
series = "2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "97--98",
booktitle = "2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023",
address = "United States",
}