Long-term heart rate variability as a predictor of patient age.

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).