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). 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.
Corino
Valentina D A
VD
Department of Biomedical Engineering, Polytechnic University of Milan, Milan, Italy. valentina.corino@polimi.it
Matteucci
Matteo
M
Cravello
Luca
L
Ferrari
Ettore
E
Ferrari
Antonio A
AA
Mainardi
Luca T
LT
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D004562:Electrocardiography; D005260:Female; D006339:Heart Rate; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D025341:Principal Component Analysis