Modelling the relationship between peripheral blood pressure and blood volume pulses using linear and neural network system identification techniques

Physiological Measurement

Volumn 20, Issue 3, 1999, Pages 287-301

  Allen, John ^a   Murray, Alan ^a  

^a FREEMAN HOSPITAL   (United Kingdom)

Author keywords

Blood pressure; Modelling; Peripheral circulation; Plethysmography; Pulse; System identification

Indexed keywords

HEMODYNAMICS; NEURAL NETWORKS; PHYSIOLOGICAL MODELS; PLETHYSMOGRAPHY; RELIGIOUS BUILDINGS;

BLOOD VOLUME PULSE; MODELING; PERIPHERAL BLOOD; PERIPHERAL CIRCULATION; PHYSIOLOGICAL DATA; PRESSURE VOLUMES; PULSE; PULSE WAVEFORMS; SYSTEM IDENTIFICATION TECHNIQUES; SYSTEM-IDENTIFICATION;

BLOOD PRESSURE;

ARTERIAL PRESSURE; ARTICLE; ARTIFICIAL NEURAL NETWORK; BLOOD VOLUME; HUMAN; LINEAR SYSTEM; MALE; NORMAL HUMAN; PERIPHERAL VASCULAR SYSTEM; PLETHYSMOGRAPHY; PRIORITY JOURNAL; PULSE PRESSURE; WAVEFORM;

EID: 0032794765     PISSN: 09673334     EISSN: None     Source Type: Journal    
DOI: 10.1088/0967-3334/20/3/306     Document Type: Article

References (28)

1. Allen J and Murray A 1995 Prospective assessment of a neural network for assessing peripheral vascular disease from photoelectric plethysmograph waveforms Physiol. Meas. 16 29-38
2. Avanzolini G, Barbini P, Capello A, Cevenini G, Pohl V and Sikora T 1989 Tracking time-varying properties of the systemic vascular bed IEEE Trans. Biomed. Eng. 36 373-81
3. Billings S A and Chen S 1995 Neural networks and system identification Neural Network Applications in Control (Institution of Electrical Engineers Control Engineering Series 53) ed G W Irwin, K Warwick and K J Hunt (London: Peregrinus) pp 229-51
4. Box G E P and Jenkins G M 1976 Transfer function models Time Series Analysis: Forecasting and Control (San Francisco: Holden-Day) pp 337-62
5. Cavallo A, Setola R and Vasca F 1996 Using MATLAB Simulink and Control systems Toolbox, a Practical Approach (The Matlab Curriculum Series) (London: Prentice-Hall) pp 237-89
6. Clark K P and Mark R G 1991 Extracting new information from the shape of the blood pressure pulse Computers in Cardiology (Los Alamitos CA: IEEE Computer Society Press) pp 225-8
7. Deswysen B, Charlier A A and Gevers M 1980 Quantitative evaluation of the systemic arterial bed by parameter estimation of a simple model Med. Biol. Eng. Comput. 18 153-66
8. Gizdulich P, Prentza A and Wesseling K H 1997 Models of brachial to finger pulse wave distortion and pressure decrement Cardiovasc. Res. 33 698-705
9. Gratz I, Kraidin J, Jacobi A G, deCastro N G and Spagna P 1992 Continuous noninvasive cardiac output as estimated from the pulse contour wave J. Clin. Monit. 8 20-7
10. Kanjilal P P 1995 Adaptive Prediction and Predictive Control (The Institution of Electrical Engineers Control Engineering Series 52) (Stevenage: Peregrinus) pp 236-60, 274-303
11. Li J K-J, Cui T and Drzewiecki G M 1990 A nonlinear model of the arterial system incorporating a pressure dependent compliance IEEE Trans. Biomed. Eng. 37 673-8
12. Ljung L 1987 System Identification - Theory for the User (Englewood Cliffs, NJ: Prentice-Hall)
13. _1991 System Identification Toolbox User's Guide (Natick, MA: The MathWorks, Inc.)
14. Ljung L and Soderstrom T 1983 Theory and Practice of Recursive Identification (Cambridge, MA: MIT Press)
15. López-Beltrán E A, Blackshear P L, Finkelstein S M and Cohn J N 1998 Non-invasive studies of peripheral vascular compliance using a non-occluding photoplethysmographic method Med. Biol. Eng. Comput. 36 748-53
16. Mills P M, Zomaya A M and Tade M O 1996 Neuro-Adaptive Process Control: a Practical Approach (Chichester: Wiley) pp 7-68
17. Molhoek P G, Wesseling K H, Arntzenius A C, Settels J J M, Volloehoven E V, Weeda H W H and deWit B 1983 Initial results of non-invasive measurement of finger blood pressure according to Penaz Automedica 4 241-6
18. Nitzan M, Babchenko A, Khonokh B and Landau D 1998 The variability of the photoplethysmographic signal - a potential method for the evaluation of the autonomic nervous system Physiol. Meas. 19 93-102
19. Nørgaard M 1997 Neural Network Based System Identification Toolbox Technical University of Denmark Department of Automation, Technical Report 97-E851
20. Ocasio W C, Rigney D C, Clark K P and Mark R 1993 Bpshape_ wk4: a computer program that implements a physiological model for analyzing the shape of blood pressure waveforms Comput. Methods Prog. Biomed. 39 169-94
21. Parthimos D, Edwards D H and Griffith T M 1996 Comparison of chaotic and sinusoidal vasomotion in the regulation of microvascular blood flow Cardiovasc. Res. 31 388-99
22. Pochet T, Gerard P, Lambermont B, Detry O, D'Orio V, Defraigne J-O, Fossion A and Limet R 1996 Selection and identification of lumped models of the arterial vasculature using multiple regression and backward elimination in the time domain (Proc. 10th Nordic-Baltic Conf. on Biomedical Engineering (Tampere 1996)) Med. Biol. Eng. Comput. 34 (suppl 1) 107-8
23. Penaz J, Honzikova N and Jurak P 1997 Vibration plethysmography: a method for studying the visco-elastic properties of finger arteries Med. Biol. Eng. Comput. 35 633-7
24. Raven F 1978 Automatic Control Engineering (Tokyo: McGraw-Hill) pp 121-63
25. Rowell L B 1993 Human Cardiovascular Control (New York: Oxford University Press)
26. Singaram M, Webb J A C, Finley C and Mitchell R H 1994 The application of PSPICE in the assessment of blood pressure pulse contour models based on Windkessel networks Automedica 16 127-38
27. Smith N T, Wesseling K H and deWit B 1985 Evaluation of two prototype devices producing non-invasive, pulsatile, calibrated blood pressure measurement from a finger J. Clin. Monit. 1 517-29
28. Wesseling K H, deWit B and Bencken J E N 1973 Arterial haemodynamic parameters derived from non-invasive recorded pulse waves, using parameter estimation Med. Biol. Eng. Comput. 6 724-31