کنفرانس بین المللی کنترل ، ابزار دقیق و اتوماسیون

صفحه اصلی / نهمین کنفرانس بین المللی کنترل ، ابزار دقیق و اتوماسیون

Vascular Age Prediction with PPG-Based LSTM Model

نویسندگان :

Kiana Pilevar Abrisham¹ Khalil Alipour² Bahram Tarvirdizaheh³ Mohammad Ghamari⁴

1- University of Tehran 2- دانشگاه تهران 3- University of Tehran 4- Kettering University

کلمات کلیدی :

Cardiovascular Disease،Long Short-Term Memory (LSTM) Network،Non-Invasive Measurements،Photoplethysmography,،Vascular Aging

چکیده :

Vascular aging encompasses structural and functional changes in blood vessels associated with aging, including increased stiffness, reduced elasticity, and atherosclerosis development. These changes contribute to cardiovascular diseases (CVDs), a global health challenge necessitating precise assessment methods. Evaluating vascular age involves comparing blood vessel health to chronological age, offering insights into arterial stiffness for potential cardiovascular disease prevention. In this study, we introduce an approach using Long Short-Term Memory (LSTM) models for vascular age prediction. Leveraging simulated Photoplethysmogram (PPG) waveforms from the radial artery and a diverse sample of healthy adults aged 25 to 75, our method offers a non-invasive, cost-effective means of health evaluation. Following 5-fold cross-validation, the LSTM-based model demonstrated significant performance, with an average test accuracy, precision, recall, and F1 Score of approximately 97%. This innovative approach, with its non-invasive nature, minimal power consumption, and user-friendly interface, showcases the potential of PPG technology's integration into various devices, such as fitness trackers, smartphones, and tablets, ensuring its widespread accessibility and applicability in real-world health assessments. Accurate vascular age prediction aids in identifying cardiovascular aging deviations and underlying disorders, advancing assessment methods and contributing to proactive cardiovascular health management.