Key result
Machine learning using second derivative PPG peak ratios classifies age groups with ~89% accuracy.
Why the study?
Can machine learning models using the second derivative of PPG signals accurately classify vascular age in human volunteers?
Observational
Can machine learning models using the second derivative of PPG signals accurately classify vascular age in human volunteers?
Effect estimate: AUC 0.95
Absolute Event Rate: 89% vs 80%
Machine learning analysis of the second derivative of PPG signals, specifically incorporating peak ratios, provides a highly accurate and non-invasive method for assessing vascular age.
May support non-invasive vascular age assessment; hypothesis-generating and requires prospective validation before clinical use.
The second derivative of the photoplethysmographic signal features characteristic peaks that provide information on vascular properties. Our analysis was conducted on deflection wave features of the second derivate of PPG signals from human volunteers aged between 26 to 63 years. We show that machine learning models can leverage these features to accurately distinguish subjects based on their age group in a binary classification approach, achieving a balanced accuracy of 80% and an AUC of 0.88 on unseen data, and 100% accuracy on a sample-level split. When including the ratios of deflection wave peaks in our feature set, accuracies of up to 89% and AUC of 0.95 were achieved. Our feature plot illustrates clear clustering between younger and older volunteers, specifically when using the peak ratios, confirming its discriminatory power and its significant potential as a non-invasive index for assessing vascular age. This study demonstrates that second derivative PPG peak ratios, together with the peak amplitudes, are a more robust marker of vascular age than the peak amplitudes alone.
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Otesteanu et al. (2025) conducted an observational in Vascular aging. Machine learning models using second derivative PPG peak ratios and amplitudes vs. Models using peak amplitudes alone was evaluated on Binary classification of age group (AUC 0.95). Machine learning models using second derivative PPG peak ratios and amplitudes accurately classified subjects by age group, achieving up to 89% accuracy and an AUC of 0.95.
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