ArticleJMIR medical informatics2022
Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study.
Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Multimodal assessment of arterial stiffness using photoplethysmography and laser Doppler flowmetry.NPJ cardiovascular health · 2026Article
- Vascular age estimation using a consumer wearable sleep tracker.PLOS digital health · 2026Article
- Data-hugging shields proprietary AI models from research that could disprove them.NPJ artificial intelligence · 2026Review
- Arterial stiffness assessment using PPG feature extraction and significance testing in an in vitro cardiovascular system.Scientific reports · 2024Article
- Photoplethysmography for the Assessment of Arterial Stiffness.Sensors (Basel, Switzerland) · 2023Review
- The Dicrotic Notch: Mechanisms, Characteristics, and Clinical Correlations.Current cardiology reports · 2023Review
- Photoplethysmogram based vascular aging assessment using the deep convolutional neural network.Scientific reports · 2022Article
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Abstract
backgroundFor the noninvasive assessment of arterial stiffness, a well-known indicator of arterial aging, various features based on the photoplethysmogram and regression methods have been proposed. However, whether because of the existing characteristics not accurately reflecting the characteristics of the incident and reflected waveforms of the photoplethysmogram or because of the lack of expressive power of the regression model, a reliable arterial stiffness assessment technique based on a single photoplethysmogram has not yet been proposed.
objectiveThe purpose of this study is to discover highly correlated features from the incident and reflected waves decomposed from a photoplethysmogram waveform and to develop an artificial neural network-based regression model for the assessment of vascular aging using newly derived features.
methodsWe obtained photoplethysmograms from 757 participants. All recorded photoplethysmograms were segmented for each beat, and each waveform was decomposed into incident and reflected waves by the Gaussian mixture model. The 26 basic features and 52 combined features were defined from the morphological characteristics of the incident and reflected waves. The regression model of the artificial neural network was developed using the defined features.
resultsIn correlation analysis, the features from the amplitude of the reflected wave and the skewness of the photoplethysmogram showed a relatively strong correlation with the participant's real age. In the estimation of real age, the artificial neural network model showed 10.0 years of root mean square error. Its estimated age and real age had a strong correlation of 0.63 (P<.001).
conclusionsThis study proved that the features defined from the reflected wave and skewness of the photoplethysmogram are useful to assess vascular aging. Moreover, the regression model of artificial neural network using these features shows the feasibility for the estimation of vascular aging.
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