Photoplethysmographic signal analysis for vascular ageing assessment

In vitro, PPG from vascular phantoms at 3 stiffness levels; in vivo, PPG from 31 healthy subjects in two age groups (<30 and >50 y, age used as a stiffness proxy). A new skew-Gaussian pulse decomposition reconstructs each pulse and yields morphological features. In vitro, 23 of 60 features differed across stiffness levels (amplitude/energy fall and reflection-timing features rise with stiffness).

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Objective . Vascular ageing (VA) is an age-related process characterised by vessel stiffening and altered endothelial function. Recognised as a major predictor of cardiovascular events, VA remains under-assessed in clinical practice due to limitations of current measurement systems. Photoplethysmography (PPG) emerges as a promising non-invasive, cost-effective tool, but its application to VA is hindered by signal complexity and lack of standardisation. This study investigated the relationship between VA and PPG waveform morphology through combined in vitro and in vivo analyses. Approach . In vitro , PPG signals were acquired from vascular phantoms with 3 stiffness levels. in vivo , PPG signals from 31 healthy subjects in two age groups (

30

years and

> 50

years) were analysed, assuming increased stiffness with age. A novel skew-Gaussian decomposition model was developed to reconstruct pulses and extract morphological features. In vitro feature differences were assessed using the Kruskal–Wallis test; in vivo data were analysed with an XGBoost classifier to discriminate age groups. Main results . The skew-Gaussian model exhibited excellent waveform reconstruction (

R

mean

2

> 0.98

). In vitro , 23 of 60 features differed significantly across stiffness levels, particularly between the stiffest phantom and the others. Amplitude and energy parameters correlated negatively with stiffness, indicating reduced vascular compliance. Temporal features related to wave reflection correlated positively, suggesting altered signal dynamics. in vivo , the XGBoost classifier achieved 87% mean accuracy, with 83% sensitivity and 92% specificity in differentiating age groups. Significance . Feature relevance analysis confirmed the physiological significance of the extracted parameters, supporting the potential of PPG as a non-invasive and scalable technology for monitoring VA.

Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: https://creativecommons.org/licenses/by/4.0/

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