Computer Methods and Programs in Biomedicine
Volume 82, Issue 3, June 2006, Pages 248-257
Long-term heart rate variability as a predictor of patient age
Corino, V.D.A., Matteucci, M., Cravello, L., Ferrari, E., Ferrari, A.A., Mainardi, L.T.
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. © 2006 Elsevier Ireland Ltd. All rights reserved.
Age prediction; Artificial neural networks; Heart rate variability