{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T21:34:03Z","timestamp":1768685643021,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>(1) Background: The objective of this study was to predict the vascular health status of elderly women during exercise using pulse wave data and Temporal Convolutional Neural Networks (TCN); (2) Methods: A total of 492 healthy elderly women aged 60\u201375 years were recruited for the study. The study utilized a cross-sectional design. Vascular endothelial function was assessed non-invasively using Flow-Mediated Dilation (FMD). Pulse wave characteristics were quantified using photoplethysmography (PPG) sensors, and motion-induced noise in the PPG signals was mitigated through the application of a recursive least squares (RLS) adaptive filtering algorithm. A fixed-load cycling exercise protocol was employed. A TCN was constructed to classify flow-mediated dilation (FMD) into \u201coptimal\u201d, \u201cimpaired\u201d, and \u201cat risk\u201d levels; (3) Results: TCN achieved an average accuracy of 79.3%, 84.8%, and 83.2% in predicting FMD at the \u201coptimal\u201d, \u201cimpaired\u201d, and \u201cat risk\u201d levels, respectively. The results of the analysis of variance (ANOVA) comparison demonstrated that the accuracy of the TCN in predicting FMD at the impaired and at-risk levels was significantly higher than that of Long Short-Term Memory (LSTM) networks and Random Forest algorithms; (4) Conclusions: The use of pulse wave data during exercise combined with the TCN for predicting the vascular health status of elderly women demonstrated high accuracy, particularly in predicting impaired and at-risk FMD levels. This indicates that the integration of exercise pulse wave data with TCN can serve as an effective tool for the assessment and monitoring of the vascular health of elderly women.<\/jats:p>","DOI":"10.3390\/s24134198","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T03:42:30Z","timestamp":1719546150000},"page":"4198","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Temporal Convolutional Neural Network-Based Prediction of Vascular Health in Elderly Women Using Photoplethysmography-Derived Pulse Wave during Exercise"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4803-7027","authenticated-orcid":false,"given":"Yue","family":"Xiao","sequence":"first","affiliation":[{"name":"Chinese Wushu Academy, Beijing Sport University, Beijing 100084, China"},{"name":"School of Physical Educantion and Sports, Sichuan Normal University, Chengdu 610101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guixian","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Wushu Academy, Beijing Sport University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4997-9083","authenticated-orcid":false,"given":"Haojie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Physical Educantion and Sports, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1159\/000495634","article-title":"Probability of at Least One High Arterial Blood Pressure Measurement in Elderly Patients with Healthy Vascular Aging in Two Years of Follow-Up","volume":"43","author":"Freitas","year":"2018","journal-title":"Kidney Blood Press. 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