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Published on: June 5, 2019
Long-term heart rate variability as a predictor of patient age
Valentina D A Corino1, Matteo Matteucci, Luca Cravello
1Department of Biomedical Engineering, Polytechnic University of Milan, Milan, Italy. valentina.corino@polimi.it
Insights
Heart rate variability (HRV) can predict a person's age using various analysis methods. Non-linear HRV metrics significantly contribute to understanding age-related changes.
Area of Science:
- Cardiology
- Biomedical Engineering
- Gerontology
Background:
- Heart rate variability (HRV) analysis is a non-invasive method to assess autonomic nervous system function.
- Aging is associated with physiological changes that can be reflected in cardiovascular parameters.
- Previous studies have explored HRV as a potential biomarker for biological aging.
Purpose of the Study:
- To investigate the potential of heart rate variability (HRV) parameters as a biomarker for estimating chronological age in a healthy population.
- To evaluate the effectiveness of different analytical models in predicting age based on HRV.
- To determine the contribution of non-linear HRV metrics to age-related modifications.
Main Methods:
- Long-term HRV analysis was conducted on 113 healthy subjects aged 20-85 years.
- Linear time and frequency domain parameters, along with non-linear HRV metrics, were computed.
- Principal Component Analysis (PCA) was employed to identify age-related HRV influences, followed by prediction using Robust Linear Regressor (RLR), Feedforward Neural Network (FFNN), and Radial Basis Function Neural Network (RBFNN).
Main Results:
- All three models achieved good age prediction accuracy (Pearson correlation coefficients: RLR=0.793, FFNN=0.872, RBFNN=0.829).
- The Feedforward Neural Network (FFNN) demonstrated the highest predictive performance.
- A tendency for overestimation of age in younger individuals and underestimation in older individuals was observed.
- Non-linear HRV indexes were found to provide important complementary information regarding age-related HRV changes.
Conclusions:
- HRV parameters, particularly when analyzed using advanced models like FFNN, show significant potential for estimating chronological age.
- Non-linear HRV metrics are crucial for a comprehensive understanding of aging-related cardiovascular autonomic regulation.
- Further research may refine these models to improve accuracy across the entire age spectrum.
Abstract:
Patients age has been estimated in healthy population by means of the heart rate variability (HRV) parameters to assess the potentiality of HRV indexes as a biomarker of age. A long-term analysis of HRV has been performed, computing linear time and frequency domain parameters as well as non-linear metrics, in a dataset of 113 healthy subjects (age range 20-85 years old). The principal component analysis has been used to capture age-related influence on HRV and then three different models have been applied to predict subjects age: a robust linear regressor (RLR), a feedforward neural network (FFNN) and a radial basis function neural network (RBFNN). A good prediction of patient age has been obtained (using all principal components, the Pearson correlation coefficient between predicted and real age: RLR=0.793; FFNN=0.872; RBFNN=0.829), even if an overestimation in younger subjects and an underestimation in older ones may be observed. The important and complementary contribution of non-linear indexes to aging related HRV modifications has also been underlined.
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