Monitoring Blood Pressure Using Regression Techniques
Blood pressure helps the physicians greatly to have a deep insight into the cardiovascular system. The determination of individual blood pressure is a standard clinical procedure considered for cardiovascular system problems. The conventional techniques to measure blood pressure (e.g. cuff method) allows a limited number of readings for a certain period (e.g. every 5-10 minutes). Additionally, these systems cause turbulence to blood flow; impeding continuous blood pressure monitoring, especially in emergency cases or critically ill persons. In this paper, the most important statistical features in the photoplethysmogram (PPG) signals were extracted to estimate the blood pressure noninvasively. PPG signals from more than 40 subjects were measured and analyzed and 12 features were extracted. The features were fed to principal component analysis (PCA) to find the most important independent features that have the highest correlation with blood pressure. The results show that the stiffness index means and standard deviation for the beat-to-beat heart rate were the most important features. A model representing both features for Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) was obtained using a statistical regression technique. Surface fitting is used to best fit the series of data and the results show that the error value in estimating the SBP is 4.95% and in estimating the DBP is 3.99%.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3669192Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 566
 A. J. Hammoudeh et al., “Prevalence of conventional risk factors in Jordanians with coronary heart disease: the Jordan Hyperlipidemia and Related Targets Study (JoHARTS),” Int. J. Cardiol., vol. 110, no. 2, pp. 179–183, Jun. 2006.
 H. Fassbender et al., “Fully implantable blood pressure sensor for hypertonic patients,” in 2008 IEEE Sensors, 2008, pp. 1226–1229.
 J. G. Webster, Medical Instrumentation: Application and Design, 4th Edition. Wiley, 2010.
 R. J. Underwood, “Blood flow and blood pressure measurement in anesthesiology using the impedance plethysmograph,” Anesth. Analg., vol. 42, pp. 217–222, Apr. 1963.
 S. Daochai, W. Sroykham, Y. Kajornpredanon, and C. Apaiwongse, “Non-invasive blood pressure measurement: Auscultatory method versus oscillometric method,” in The 4th 2011 Biomedical Engineering International Conference, 2011, pp. 221–224.
 P. Zurek, O. Krejcar, M. Penhaker, M. Cerny, and R. Frischer, “Continuous Noninvasive Blood Pressure Measurement by Near Infra Red CCD Camera and Pulse Transmit Time Systems,” in 2010 Second International Conference on Computer Engineering and Applications, 2010, vol. 2, pp. 449–453.
 W. T. Kemmerer, R. W. Ware, H. F. Stegall, J. L. Morgan, and R. Kirby, “Blood pressure measurement by Doppler ultrasonic detection of arterial wall motion,” Surg. Gynecol. Obstet., vol. 131, no. 6, pp. 1141–1147, Dec. 1970.
 G. M. Drzewiecki, J. Melbin, and A. Noordergraaf, “Arterial tonometry: review and analysis,” J. Biomech., vol. 16, no. 2, pp. 141–152, 1983.
 J. G. Thomas, “A method for continuously indicating blood pressure,” J. Physiol., vol. 129, no. 3, p. 75–76P, Sep. 1955.
 S. Loukogeorgakis, R. Dawson, N. Phillips, C. N. Martyn, and S. E. Greenwald, “Validation of a device to measure arterial pulse wave velocity by a photoplethysmographic method,” Physiol. Meas., vol. 23, no. 3, pp. 581–596, Aug. 2002.
 M. Heravi, M. Khalilzadeh, “Designing and Constructing an Optical System to measure Continuous and Cuffless Blood Pressure Using Two Pulse Signals”, Iranian Journal of Medical Physics, Vol. 10, No. 4, Autumn 2013 & Vol. 11, No. 1, Winter 2014, 215-223.
 B. Gribbin, A. Steptoe, P. Sleight, “Pulse wave velocity as a measure of blood pressure change”, Psychophysiology, v.13, n.1, p.86–90, 1976.
 J. Allen, “Photoplethysmography and its application in clinical physiological measurement,” Physiol. Meas., vol. 28, no. 3, p. R1, 2007.
 G. Fortino and V. Giampà, “PPG-mode methods for non-invasive and continuous blood pressure measurement: an overview and development issues in body sensor networks,” in 2010 IEEE International Workshop on Medical Measurements and Applications, 2010, pp. 10–13.
 X. F. Teng and Y. T. Zhang, “Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach,” in the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2003, vol. 4, p. 3153–3156.
 Ahmet Reşit Kavsaoğlu, Kemal Polat and Mehmet Recep Bozkurt, “An innovative peak detection algorithm for photoplethysmography signals: An adaptive segmentation method”, Turk J Elec Eng & Comp Sci 24(3):1782-179 (2016)