Non-linear multivariate modeling of cerebral hemodynamics with autoregressive Support Vector Machines

Medical Engineering and Physics

Volumn 33, Issue 2, 2011, Pages 180-187

  Chacon, Max ^a   Araya, Claudio ^a   Panerai, Ronney B ^b  

^a UNIVERSIDAD DE SANTIAGO DE CHILE   (Chile)

^b UNIVERSITY OF LEICESTER   (United Kingdom)

Author keywords

Autoregressive models; Hemodynamics; PaCO2; Support Vector Machines

Indexed keywords

AR MODELS; ARTERIAL BLOOD PRESSURE; AUTO REGRESSIVE MODELS; AUTO-REGRESSIVE; CEREBRAL BLOOD FLOW; CEREBRAL HEMODYNAMICS; CORRELATION COEFFICIENT; FINITE IMPULSE RESPONSE MODEL; HEALTHY SUBJECTS; METABOLIC MECHANISM; MULTIVARIATE AUTOREGRESSIVE; NON-LINEAR; NON-LINEAR MODEL; PACO2; REGULATORY MECHANISM;

BLOOD; BLOOD PRESSURE; HEMODYNAMICS; HYDRODYNAMICS; IMPULSE RESPONSE; MODEL STRUCTURES; VECTORS;

SUPPORT VECTOR MACHINES;

CARBON DIOXIDE;

ADULT; ARTERIAL CARBON DIOXIDE TENSION; ARTERIAL PRESSURE; ARTICLE; BRAIN BLOOD FLOW; BREATHING MECHANICS; CAPNOGRAPHY; CONTROLLED STUDY; DOPPLER ECHOGRAPHY; HUMAN; MATHEMATICAL ANALYSIS; MULTIVARIATE ANALYSIS; NONLINEAR SYSTEM; NORMAL HUMAN; PRIORITY JOURNAL; PROCESS MODEL; REGULATORY MECHANISM; SUPPORT VECTOR MACHINE; VOLUNTEER;

BLOOD FLOW VELOCITY; BLOOD PRESSURE; CARBON DIOXIDE; CEREBROVASCULAR CIRCULATION; FEEDBACK, PHYSIOLOGICAL; HEMODYNAMICS; HOMEOSTASIS; HUMANS; INHALATION; MODELS, CARDIOVASCULAR; MULTIVARIATE ANALYSIS; NONLINEAR DYNAMICS; REST;

EID: 79951771125     PISSN: 13504533     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.medengphy.2010.09.023     Document Type: Article

References (31)

1. Paulson O.B., Strandgaard B., Edvinsson L. Cerebral autoregulation. Cerebrovasc Brain Metab Rev 1990, 2:161-192.
2. Aaslid R., Lindegaard K.F., Sorteberg W., Nornes H. Cerebral autoregulation dynamics in humans. Stroke 1989, 20:45-52.
3. Panerai R.B. Assesment of cerebral pressure autoregulation in humans-a review of measurement methods. Physiol Meas 1998, 19:305-338.
4. Markwalder T.H., Grollmund P., Seiler R.W., Roth F., Aaslid R. Dependency of blood flow velocity in the middle cerebral artery on end-tidal carbon dioxide partial pressure: a transcranial ultrasound Doppler study. J Cereb Blood Flow Metab 1984, 4:368-372.
5. Poulin M.J., Robbins P.A. Indexes of flow and cross-sectional area of the middle cerebral artery using Dopler ultrasound during hypoxia and hypercapnia in humans. Stroke 1996, 27:2244-2250.
6. Panerai R.B., Simpson D.M., Deverson S.T., Mahony P., Hayes P., Evans D.H. Multivariate dynamic analysis of cerebral blood flow regulation in humans. IEEE Trans Biomed Eng 2000, 47:419-423.
7. 2. Med Biol Eng Comput 2000, 38:535-539.
8. 2. Amer J Physiol: Regul Integr Compar Physiol 2004, 287:627-632.
9. 2 variations on cerebral blood flow in healthy humans. IEEE Trans Biomed Eng 2004, 51:1932-1943.
10. 2. Cerebrovasc Dis 1999, 9:146-151.
11. 2 reactivity testing without blood pressure monitoring?. Stroke 1999, 30:398-401.
12. Marmarelis V.Z. Identification of nonlinear biological systems using Laguerre expansions of kernels. Ann Biomed Eng 1993, 21:573-589.
13. Suykens J.A.K., Vandewalle J. Recurrent least squares support vector machines. IEEE Trans Circuits Syst I 2000, 47:1109-1114.
14. Martínez-Ramón M., Rojo-Álvarez J.L., Camps-Valls G., Navia-Vázquez A., Soria-Olivas E., Figueiras-Vidal A. Support vector machines for nonlinear kernel ARMA system identification. IEEE Trans Neural Networks 2006, 17:1617-1622.
15. Espinoza M., Suykens J.K.A., Belmans R., De Moor B. Electric load forecasting-using kernel based modeling for nonlinear system identification. IEEE Control Syst Mag 2007, 43-57. [Special Issue on Applications of System Identification].
16. Schölkopf B., Smola A., Williamson R.C., Bartlett P.L. New support vector algorithms. Neural Comput 1998, 12:1083-1121.
17. Vapnik V., Golowich S., Smola A. Support vector method for function approximation. Regression estimation, and signal processing. Neural information processing systems 1997, 9:281-287. MIT Press, Cambridge, MA.
18. Bishop C.M. Neural networks for pattern recognition 1997, Oxford University Press, New York.
19. Hsu CW, Chang CC, Lin CJ. A practical guide to support vector classification. Technical Report: Department of Computer Science, National Taiwan University; 2003.
20. Ramos E.G., Simpson D.M., Panerai R.B., Nadal J., Lopez J.A.M., Evans D.H. Objective selection of signal for assessment of cerebral blood flow autoregulation in neonates. Physiol Meas 2006, 27:35-49.
21. Panerai R.B. Cerebral autoregulation: from models to clinical applications. Cardiovasc Eng 2008, 8:42-59.
22. Panerai R.B., Chacon M.M., Pereira R.R., Evans D.H.D.H. Neural network modelling of dynamic cerebral autoregulation: assessment and comparison with established methods. Med Eng Phys 2004, 26:43-52.
23. Peng T., Rowley A.B., Ainslie P.N., Poulin M.J., Payne S.J. Multivariate system identification for cerebral autoregulation. Ann Biomed Eng 2008, 36:308-320.
24. Payne S.J. A model of the interaction between autoregulation and neural activation in the brain. Math Biosci 2006, 204:260-281.
25. 2 reactivity, and intracranial pressure: a mathematical model. Amer J Physiol: Heart Circ Physiol 1998, 274:H1715-H1728.
26. Payne S.J., Tarassenko L. Combined transfer function analysis and modelling of cerebral autoregulation. Ann Biomed Eng 2006, 34:847-858.
27. Tiecks F.P., Lam A.M., Aaslid R., Newell D.W. Comparison of static and dynamic cerebral autoregulation measurements. Stroke 1995, 26:1014-1019.
28. Liu J., Simpson D.M., Allen R. High spontaneous fluctuation in arterial blood pressure improves the assessment of cerebral autoregulation. Physiol Meas 2005, 26:725-741.
29. Panerai R.B., Carey B.J., Potter J.F. Short-term variability of cerebral blood flow velocity responses to arterial blood pressure transients. Ultrasound Med Biol 2003, 29:31-38.
30. Panerai R.B., Eames P.J., Potter J.F. Variability of time-domain indices of dynamic cerebral autoregulation. Physiol Meas 2003, 24:367-381.
31. Giller C.A., Mueller M. Linearity and non-linearity in cerebral hemodynamics. Med Eng Phys 2003, 25:633-646.