Analysis of Heart Rate Variability to Predict Patient Age in a Healthy Population

V. D. A. Corino; M. Matteucci; L. T. Mainardi

doi:10.1055/s-0038-1625405

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2007; 46(02): 191-195
DOI: 10.1055/s-0038-1625405

Original Article

Schattauer GmbH

Analysis of Heart Rate Variability to Predict Patient Age in a Healthy Population

V. D. A. Corino

¹Department of Biomedical Engineering, Politecnico di Milano, Milan, Italy

,

M. Matteucci

²Department Electronic and Information, Politecnico di Milano, Milan, Italy

,

L. T. Mainardi

¹Department of Biomedical Engineering, Politecnico di Milano, Milan, Italy

› Author Affiliations

Abstract

Summary

Objectives : 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.

Keywords

Age prediction - heart rate variability

Full Text

References

References
1. Bigger J, Steinman RC, Rolnitzky LM, Fleiss JL, Albrecht P, Cohen RJ. Power law behaviour of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants. Circulation 1996; 93: 2142-2151.
2. Pincus SM. Approximate entropy as ameasure of system complexity. Proc Natl Acad Sci USA 1991; 88: 2297-2301
3. Peng CK, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 1995; 5: 82-87.
4. Pikkujamsa SM, Makikallio TH, Sourander LB, Raiha IJ, Puukka P, Skytta J, Peng CK, Goldberger AL, Huikuri HV. Cardiac interbeat interval dynamics from childhood to senescence: comparison of conventional and new measures based on fractals and chaos theory. Circulation 1999; 100: 393-399.
5. Piccirillo G, Bucca C, Bauco C, Cinti AM, Michele D, Fimognari FL, Cacciafesta M, Marigliano V. Power spectral analysis of heart rate in subjects over a hundred years old. Int J Cardiol 1998; 63: 53-61.
6. Antelmi I, de Paula RS, Shinzato AR, Peres CA, Mansur AJ, Grupi CJ. Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease. Am J Cardiol 2004; 93: 381-385.
7. Colosimo A, Giuliani A, Mancini AM, Piccirillo G, Marigliano V. Estimating a cardiac age by means of heart rate variability. Am J Physiol 1997; 273: H1841-H1847.
8. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology.. Heart rate variability: stan dards of measurement, physiological interpretation, and clinical use. Circulation 1996; 93: 1043-1065.
9. Sakata S, Hayano J, Mukai S, Okada A. Fucinami T Aging and spectral characteristics of the nonharmonic component of 24-h heart rate variability. Am J Physiol Regul Integr Comp Physiol 1999; 276: 1724-1731.
10. Jolliffe IT. Principal component analysis (2nd edition). New York: Springer; 2002
11. Bishop CM. Neural Networks for Pattern Recognition. Oxford University Press, Inc; 1995
12. Haykin S. Neural Networks. A Comprehensive Foundation (2nd edition). New Jersey, USA: Prentice Hall; 1999
13. Caruana R, Lawrence S, Giles CL. Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. In: Proceedings of Neural Information Processing Systems 2000: 402-408.
14. Marquardt D. An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 1963; 11: 431-441.
15. Hagan MT, Menhaj M. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 1994; 5: 989-993.
16. The Normal Sinus Rhythm RR Interval Database and The MIT-BIH Normal Sinus Rhythm Database. http://www.physionet.org/physiobank/database/nsr2db/