Non-Intrusive Device for Real-Time Circulatory System Assessment with Advanced Signal Processing Capabilities

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Non-Intrusive Device for Real-Time Circulatory System Assessment with Advanced Signal Processing Capabilities

This paper presents a device that uses three cardiography signals to characterize several important parameters of a subject's circulatory system. Using electrocardiogram, finger photoplethysmogram, and ballistocardiogram, three heart rate estimates are acquired from beat-to-beat time interval extraction. Furthermore, pre-ejection period, pulse transit time (PTT), and pulse arrival time (PAT) are computed, and their long-term evolution is analyzed. The system estimates heart rate variability (HRV) and blood pressure variability (BPV) from the heart rate and PAT time series, to infer the activity of the cardiac autonomic system. The software component of the device evaluates the frequency content of HRV and BPV, and also their fractal dimension and entropy, thus providing a detailed analysis of the time series' regularity and complexity evolution, to allow personalized subject evaluation.

Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996). Heart rate variability - standards of measurement, physiological interpretation, and clinical use. Circulation, 93 (5), 1043-1065.

Parati, G., Saul, J. P., Rienzo, M. D., Mancia, G. (1995). Spectral analysis of blood pressure and heart rate variability in evaluating cardiovascular regulation: a critical appraisal. Hypertension, 25, 1267-1286.

Berntson, G., Cacciopo, J., Quigley, K., Fabro, V. (1994). Autonomic space and psychophysiological response. Psychophysiology, 31 (1), 44-61.

Dawson, S. L., Manktelow, B. N., Robinson, T. G., Panerai, R. B., Potter, J. F. (2000). Which parameters of beat-to-beat blood pressure and variability best predict early outcome after acute ischemic stroke? Stroke, 31, 463-468.

American College of Cardiology Cardiovascular Technology Assessment Committee. (1993). Heart rate variability for risk stratification of life-threatening arrhythmias. J. Am. Coll. Cardiology, 22, 948-950.

Postolache, O., Girão, P. S., Postolache, G. (2007). New approach on cardiac autonomic control estimation based on BCG processing. In Canadian Conference on Electrical and Computer Engineering. Vancouver, Canada, IEEE, 876-879.

Postolache, O., Postolache, G., Girão, P. (2007). New device for assessment of autonomous nervous system functioning in psychophysiology. In IEEE International Workshop on Medical Measurements and Applications. Warsaw, Poland, IEEE, 1-5.

Pinheiro, E. C., Postolache, O. (2008). Heart rate variability virtual sensor application in blood pressure assessment system. In Biomedical Engineering : Proceedings of the 6th IASTED International Conference. Innsbruck, Austria, Acta Press, 79-82.

Muehslteff, J., Espina, J., Alonso, M., Aubert, X., Falck, T. (2008). Wearable body sensor network for continuous context-related pulse arrival time monitoring. In Biomedical Engineering : Proceedings of the 6th IASTED International Conference. Innsbruck, Austria, Acta Press, 378-383.

Geddes, L. A., Voelz, M., James, S., Reiner, D. (1981). Pulse arrival time as a method of obtaining systolic and diastolic blood pressure indirectly. Med. Biol. Eng. Comput., 19, 671-672.

Ma, T., Zhang, Y. T. (2005). A correlation study on the variabilities in pulse transit time, blood pressure, and heart rate recorded simultaneously from healthy subjects. In IEEE EMBS 27th Annual Conference. Shanghai, China, IEEE, 996-999.

Chen, W., Kobayashi, T., Ichikawa, S., Takeuchi, Y., Togawa, T. (2000). Continuous estimation of systolic blood pressure using the pulse arrival time and intermittent calibration. Med. Biol. Eng. Comput., 38 (5), 569-574.

Sugo, Y., Tanaka, R., Soma, T., Kasuya, H., Sasaki, T., Sekiguchi, T., Hosaka, H., Ochiai, R. (1999). Comparison of the relationship between blood pressure and pulse wave transit times at different sites. In 1st Joint BMES/EMBS Conference Serving Humanity, Advancing Technology. Atlanta, USA, IEEE, 222.

Steptoe, A., Smuylan, H., Gribbin, B. (1976). Pulse wave velocity and blood pressure change: calibration and applications. Psychophysiology, 13 (5), 488-493.

Gribbin, B., Steptoe, A., Sleight, P. (1976). Pulse wave velocity as a measure of blood pressure change. Psychophysiology, 13 (1), 86-90.

Espina, J., Falck, T., Muehlsteff, J., Aubert, X. (2006). Wireless body sensor network for continuous cuff-less blood pressure monitoring. In 3rd IEEE EMBS International Summer School and Symposium on Medical Devices and Biosensors. Boston, USA, IEEE, 11-15.

Pinheiro, E. C., Postolache, O., Girão, P. (2009). Pulse arrival time and ballistocardiogram application to blood pressure variability estimation. In IEEE International Workshop on Medical Measurement and Applications. Cetraro, Italy, IEEE, 132-135.

Kiu, Y., Poon, C., Zhang, Y. (2008). A hydrostatic calibration method for the design of wearable PAT-based blood pressure monitoring devices. In IEEE EMBS 30th Annual Conference. Vancouver, Canada, IEEE, 1308-1310.

Skinner, J., Anchin, J., Weiss, D. (2008). Nonlinear analysis of the heartbeats in public patient ECGs using an automated PD2i algorithm for risk stratification of arrhythmic death. Ther. Clin. Risk Manag., 4 (2), 549-557.

Yeragani, V. K., Srinivasan, K., Vempati, S., Pohl, R., Balon, R. (1993). Fractal dimension of heart rate time series: an effective measure of autonomic function. J. Appl. Phys., 75 (6), 2429-2438.

Skinner, J., Pratt, C., Vybiral, T. (1993). A reduction in the correlation dimension of heartbeat intervals precedes imminent ventricular fibrillation in human subjects. Am. Heart J., 125 (3), 731-743.

Vybiral, T., Skinner, J. (1993). The point correlation dimension of R-R Intervals predicts sudden cardiac death among high-risk patients. In Computers in Cardiology, London, UK, IEEE, 257-260.

Storella, R., Wood, H., Mills, K., Kanters, J., Højgaard, M., Holstein-Rathlou, N. (1998). Approximate entropy and point correlation dimension of heart rate variability in healthy subjects. Integr. Physiol. Behav. Sci., 33 (4), 315-320.

Yeragani, V., Sobolewski, E., Jampala, V., Kay, J., Yeragani, S., Igel, G. (1998). Fractal dimension and approximate entropy of heart period and heart rate: awake versus sleep differences and methodological issues. Clin. Sci., 95 (3), 295-301.

Perkiömäki, J., Mäkikallio, T., Huikuri, H. (2005). Fractal and complexity measures of heart rate variability. Clin. Exp. Hypertens., 27 (2-3), 149-158.

Richman, J., Moorman, J. (2000). Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol., 278 (6), 2039-2049.

Rosso, O., Martin, M., Figliola, A., Keller, K., Plastino, A. (2006). EEG analysis using wavelet-based information tools. J. Neurosci. Methods, 153 (2), 163-182.

Lunak, D. R., Bryngelson, R. S. (2006). Noninvasive Blood Pressure Monitor Having Automatic High Motion Tolerance. U. S. Patent No. 7,052,465. Washington, D. C.: U. S. Patent and Trademark Office.

Foo, J. Y. A., Lim, C. S. (2006). Pulse transit time as an indirect marker for variations in cardiovascular related reactivity. Technol. Health Care, 14 (2), 97-108.

Muehlsteff, J., Aubert, X., Schuett, M. (2006). Cuffless estimation of systolic blood pressure for short effort bicycle tests: the prominent role of the pre-ejection period. In IEEE EMBS 28th Annual Conference. New York, USA, IEEE, 5088-5092.

Payne, R. A., Symeonides, C. N., Webb, D. J., Maxwell, S. R. J. (2006). Pulse transit time measured from the ECG: an unreliable marker of beat-to-beat blood pressure. J. Appl. Physiol., 100, 136-141.

Pinheiro, E. C., Postolache, O., Girão, P. (2010). Theory and developments in an unobtrusive cardiovascular system representation: Ballistocardiography. Open Biomed. Engin. J., 4, 201-216.

Schroeder, M. (1991). Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise. New York, USA: W. H. Freeman.

Costa, M., Goldberg, A. L., Peng, C. K. (2002). Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Lett., 89 (6), 021906.

Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA, 88, 2297-2301.

Richman, J. S., Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol., 278 (6), 2039-2049.

Goldberger, A. L., Peng, C. K., Lipsitz, L. A. (2002). What is physiologic complexity and how does it change with aging and disease? Neurobiol. Aging, 23 (1), 23-26.

Lekkala, J., Paajane, M. (1999). EMFi - new electret material for sensors and actuators. In 10th IEEE International Symposium on Electrets. Delphi, Greece, IEEE, 743-746.

Strong, P. (1970). Biophysical Measurements. Beaverton, USA: Tektronix.

Pinheiro, E. C., Postolache, O., Girão, P. (2010). Automatic wavelet detrending benefits to the analysis of cardiac signals acquired in a moving wheelchair. In 32nd Annual International Conference of the IEEE EMBS. Buenos Aires, Argentina, IEEE, 602-605.

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