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Related Experiment Video

Updated: May 8, 2026

Measuring the Carotid to Femoral Pulse Wave Velocity (Cf-PWV) to Evaluate Arterial Stiffness

Measuring the Carotid to Femoral Pulse Wave Velocity (Cf-PWV) to Evaluate Arterial Stiffness

Published on: May 3, 2018

New photoplethysmographic signal analysis algorithm for arterial stiffness estimation.

Kristjan Pilt¹, Rain Ferenets, Kalju Meigas

¹Department of Biomedical Engineering, Technomedicum, Tallinn University of Technology, Tallinn, Estonia. kristjan.pilt@cb.ttu.ee

Thescientificworldjournal

|August 29, 2013

Summary

This study introduces an improved algorithm for calculating the ageing index (AGI) from photoplethysmography waveforms, enhancing arterial stiffness assessment. The optimized method offers more accurate differentiation of cardiovascular ageing in healthy individuals and diabetes patients.

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Pulse Wave Velocity Testing in the Baltimore Longitudinal Study of Aging

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Measuring the Carotid to Femoral Pulse Wave Velocity (Cf-PWV) to Evaluate Arterial Stiffness

Measuring the Carotid to Femoral Pulse Wave Velocity (Cf-PWV) to Evaluate Arterial Stiffness

Published on: May 3, 2018

Pulse Wave Velocity Testing in the Baltimore Longitudinal Study of Aging

Pulse Wave Velocity Testing in the Baltimore Longitudinal Study of Aging

Published on: February 7, 2014

Area of Science:

Biomedical Engineering
Cardiovascular Physiology
Medical Signal Processing

Background:

Premature arterial stiffening is a key indicator for cardiovascular disease risk.
The ageing index (AGI), derived from second derivative photoplethysmography (SDPPG), is used to assess arterial stiffness and cardiovascular ageing.
Existing SDPPG analysis methods require refinement for improved accuracy.

Purpose of the Study:

To develop and validate a new SDPPG analysis algorithm with optimized filtering and signal normalization.
To enhance the accuracy of the ageing index (AGI) calculation for better arterial stiffness differentiation.
To evaluate the proposed algorithm's effectiveness in distinguishing between healthy subjects and diabetes patients.

Main Methods:

Optimization of filter parameters (low-pass edge frequency and transition band) for minimal AGI standard deviation.
Application of signal normalization in the time domain for SDPPG waveform analysis.
Validation of the algorithm on a cohort of 21 healthy subjects and 20 diabetes patients.

Main Results:

Optimal filter parameters identified as 6 Hz edge frequency and 1 Hz transition band, yielding an AGI standard deviation of 0.06.
A strong linear correlation (r = 0.91) was established between subject age and calculated AGI.
A significant difference in mean AGI was observed between healthy individuals and diabetes patients (P < 0.0005).

Conclusions:

The proposed SDPPG analysis algorithm with optimized filtering provides a more effective tool for assessing arterial stiffness.
The enhanced AGI calculation method demonstrates potential for early identification of cardiovascular ageing and disease risk.
This approach offers a valuable, non-invasive method for differentiating arterial stiffness levels in clinical settings.