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Abstract

Cardiovascular disease (CVD) remains the leading cause of death globally, with arterial stiffness recognised as a key predictor of vascular ageing and disease progression. While photoplethysmography (PPG) shows promise as a non-invasive tool for vascular health monitoring, most research relies on in vivo data, limiting the ability to systematically examine individual physiological factors. This thesis presents a custom-built in vitro testing platform designed to isolate and evaluate the impact of arterial stiffness on PPG signals under controlled conditions.

Custom silicone vessels with adjustable stiffness values, embedded within soft tissue phantoms, were fabricated and tested using a bilateral flow system. The platform enabled direct comparison of healthy and unhealthy vessels under identical haemodynamic environments, producing consistent and reproducible PPG signals across a range of flow settings. A comprehensive feature extraction pipeline was established and validated, identifying PPG waveform features most sensitive to stiffness variation.

The findings demonstrate that PPG features respond dynamically to vascular stiffness and flow conditions, offering a foundation for data-driven algorithm development. Studies across varying vessel stiffnesses showed a pronounced reduction in key morphological features, including amplitude, pulse width, and area under the curve, alongside increases in specific signal indices such as kurtosis, skewness and zero-crossing rate. When examined under different flow rates, these changes were more pronounced in healthy vessels than in their stiffer counterparts, highlighting the heightened sensitivity of PPG features to haemodynamic changes in healthy vessels.

The platform provides a structured environment for generating labelled datasets to support machine learning applications, bridging the gap between controlled experimentation and real-world cardiovascular monitoring. This work contributes to the development of reliable, accessible diagnostic tools for early detection and long-term tracking of vascular health.

Publication Type:	Thesis (Doctoral)
Subjects:	Q Science > Q Science (General) R Medicine > RB Pathology
Departments:	School of Science & Technology > Department of Engineering School of Science & Technology > School of Science & Technology Doctoral Theses Doctoral Theses

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