Analysis of heart rate variability to predict patient age in a healthy population.
OBJECTIVE
To estimate age of healthy subjects by means of the heart rate variability (HRV) parameters thus assessing the potentiality of HRV indexes as a biomarker of age.


METHODS
Long-term indexes of HRV in time domain, frequency domain and non-linear parameters were computed on 24-hour recordings in a dataset of 63 healthy subjects (age range 20-76 years old). Then, as interbeat dynamics markedly change with age, showing a reduced HRV in older subjects, we tried to capture age-related influence on HRV by principal component analysis and to predict the subject age by means of a feedforward neural network.


RESULTS
The network provides good prediction of patient age, even if a slight overestimation in the younger subjects and a slight underestimation in the older ones were observed. In addition, the important contribution of non-linear indexes to prediction is underlined.


CONCLUSIONS
HRV as a predictor of age may lead to the definition of a new biomarker of aging.
Corino
V D A
VD
Department of Biomedical Engineering, Politecnico di Milano, Via Golgi 39, 20133 Milano, Italy. valentina.corino@polimi.it
Matteucci
M
M
Mainardi
L T
LT
D000328:Adult; D000367:Age Factors; D000368:Aged; D000375:Aging / Q000502:physiology*; D001341:Autonomic Nervous System; D015415:Biomarkers; D002940:Circadian Rhythm / Q000502:physiology*; D015716:Electrocardiography, Ambulatory; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011153:Population; D044382:Population Groups; D012815:Signal Processing, Computer-Assisted; D013995:Time
D015415:Biomarkers