Monitoring blood vital bio signs using secondary speckle patterns

Talia Sirkis; Yevgeny Beiderman; Sergey Agdarov; Yafim Beiderman; Zeev Zalevsky

doi:10.1364/OE.24.027899

1. Introduction

Cardiovascular diseases are currently classified as one of the major causes of human mortality. New methods of detection and monitoring human cardiovascular condition have an important value. Mechanical properties of the pulmonary system, particularly arteries, are important factors in determining the physiological health of a patient. For example, Pulse Wave Velocity (PWV) is widely used as an index of arterial distensibility [1]. Moreover, previous works showed that aortic PWV is strongly associated with presence and extent of atherosclerosis and constitutes a forceful marker and predictor of cardiovascular risk in hypertensive patients [2]. Relationship between aortic pulse wave velocity and stiffness based on local diameter and pressure in the common carotid and femoral arteries was also obtained [3]. It was shown [4] that PWV is a marker of arterial stiffness and is associated with higher cardiovascular (CV) mortality, coronary heart disease (CHD) and stroke.

Other important cardiac parameters are blood pressure (BP) and blood pulse pressure (PP). They are both significant and most common factors reflecting condition of the cardiovascular system [4]. BP and PP vary depending on many factors even during routine measurement by pressure cuff placed over clothing. None resting, emotional state, full bladder and other factors could affect routine measurement and cause blood pressure fluctuations [5, 6]. Therefore, experts need for best diagnostic, to base conclusions on long term and preferably continuous measurements [7]. In Korotkoff technique for measuring blood pressure, the brachial artery is occluded by a cuff placed around the upper arm and inflated to above systolic pressure. As it is gradually deflated, pulsatile blood flow is re-established and accompanied by sounds that can be detected by a stethoscope held over the artery just below the cuff [4]. Such method allows monitoring for short periods only, since pressure must be released to allow blood flow into the arm [8]. Therefore, in a number of papers, different approaches for continuous noninvasive BP and PP monitoring have been presented [9–11].

Gribbin et al. [8] examined the relationship between PWV and arterial pressure. It was found that PWV increases in a linear fashion with arterial transmural pressure (TMP) raise. Gribbin et al. predicted and found that changes in PWV can be used to detect and to follow up changes in BP. It should be mentioned that the authors didn’t receive evidence for the validity of the correlations with increasing age or resting pressure. Moreover, they discussed factors, other than an increase in distending pressure, which may influence the changes in arterial distensibility and hence in PWV. It was assumed that the results haven't been caused by changes in autonomic tone, because the heart rates and blood pressures of all tested subjects did not variate significantly. The relation between viscoelasticity and PWV was neglected and the heart rate variations were not sufficient. Y. S. Sugai et al. [12] found that heart rate of 40-90 bpm didn’t affect PWV. Similar results were found by P. Lantelme et al [13]. Samer S. Najjar et al. found that arterial stiffness, as indexed by PWV, is an independent predictor of the longitudinal increase in systolic blood pressure (SBP) [14].

Nowadays, one way to measure PWV is by using imaging methods such as MRI and ultra-sound (US), however these methods are very expensive and are not easy to apply. Some imaging methods, which use pressure mechano-transducers, are also available for PWV measurements, but those have errors associated with distance estimation between the waveforms. Due to low precision and PWV measurements complications, optical methods are being developed. Optical methods are cheaper and allow comfortable and non-contact measurement [15].

The currently used tonometer is a pressure sensor which is used as the gold standard method for measuring PWV. However the measurement requires operating skills which could influence the measurement precision. Due to this disadvantage, the method is mainly used in research and clinical settings by trained operators, but it is not widespread in clinical or in ambulatory practice. It is common for many technics to measure the wave pathway on the body surface. Its largely depends on body structure, thus introducing a significant systematic error in the PWV estimation. Photo-plethysmography also represents a non-invasive method for the detection of pressure waves, but the waves obtained by this method don’t give reliable information. An imaging method is the ultra sound (US) scan, which estimates the time delay between the diameters waveform recorded simultaneously at two close positions along the vessels. This technique depends on reliable identification of the foot of the diameter waveforms and a sufficiently high sampling frequency. The MRI is also a PWV measurement method, but as mentioned, it is a laboratory based expensive tool [15].

Optical sensors are broadly used in heart beats rate and in blood-pressure-related diagnostics. Cardiovascular pulsation causes a change in the amount of blood flux in the vessels. Therefore, many optical sensors were used to measure variation of the optical power, which is affected by the change in blood flux [16]. Meigas et al, used photo-detection in a diode laser in order to detect pulsation profile and PWV [17].

Photo plethysmography (PPG) is a non-invasive optical method to detect a cardiovascular pulse wave travelling through the body. The method requires a light source and a photodetector to measure small variations in light intensity after light interaction with the illuminated part. The authors of Refs [18, 19] present a methodology for formation of PPG images from video recordings of living body in the reflection geometry. The main purpose of the paper was to demonstrate visualization of dynamic changes in cardiovascular pulse wave during the cardiac cycle.

Zalevsky et al. presented a novel technique for remote noncontact blood pulse pressure measurement that is based on tracking the temporal changes in both the position and the amplitude of reflected secondary speckle patterns produced in human skin when illuminated by a laser beam [16]. Speckles are self-interference random patterns where each individual speckle serves as a reference point from which it is possible to track changes in the phase of the light being scattered from a surface.

Moreover, since the speckles are self-interfering patterns the detection is done by simple imaging so the detection module is not an interferometer and thus it is less sensitive to noises. It was proposed by Zalevsky et al. to have the camera focused on the far field and the object itself being defocused. Such configuration transforms vibrations of the object to a lateral shift of the speckles pattern. In this way, the movement of the object creates a situation in which the speckle pattern is only moving or vibrating in the transversal plane. The authors extracted heart beats temporal signature [20] using the reflected secondary speckle patterns.

It was already shown that many advantages are expected in the field of sensing based upon speckle-polarization effects providing additional selectivity and sensitivity to tissue structure. The speckle technique is also a very promising instrument for investigation of dynamics of living objects and can be applicable in many areas of medicine [21, 22].

In this article for the first time the PWV and PP are measured continuously by processing the secondary speckles pattern and analyzing its relationship. The basic model shows that both parameters have an influence on BP. The main goal of this work is to offer a novel, easy and low cost speckle based measurement which will indicate changes in condition of the cardiovascular system and BP just by having an initial individual calibration and continuous monitoring of the PWV. Note that the heart beat and amplitude of body vibration are assumed to be proportional to the heart stroke volume.

2. Theoretical background

The heart generates pulse pressure which propagates throughout the arterial tree. The speed of the propagation depends on the blood vessel elasticity and geometry, such as thickness, diameter and material. Those are the main parameters affecting the PWV. Example of a model showing the relation between the parameters is Moens-Korteweg equation:

PWV = \sqrt{\frac{Eh}{ρd}}

where E is Young's modulus of the arterial wall; h is wall thickness; d is the arterial diameter; ρ is the blood density [1]. It can be seen according to this equation that the PWV increase with stiff arteries. Vessels become stiffer with increasing age, and diseased arteries are stiffened more than healthy arteries [23].

The PWV is calculated from pulse wave transit time (t) between two measuring points and corresponding blood vessels length ( $l$ ). In our research average PWV will be calculated using:

PWV = \frac{l}{t} [m / sec]

Main factors affecting BP, PP and PWV could be classified as follows:

1. Cardiac output

Cardiac output is calculated according to the following equation:

Q = HR \times SV [\frac{L}{min}]

Where HR is heart rate (1/min) and SV is heart stroke volume (L). P. Lantelme et al [13] obtained that HR doesn’t influence BP but does influence PWV. The study shows that HR exerts a significant influence on PWV assessment in elderly subjects. The findings also indicate that HR may also have an effect on PWV independent of BP level. A similar conclusion was found by Kikuya et al [24]. Previous works showed that reduced values of the ratio between SV and PP as a percentage of predicted by individual body size, age, and heart rate is a predictor of cardiovascular morbidity independent of age and presence of hypertrophy in arterial hypertension [25]. The cardiac output components do not have strong correlation with the BP. For example, it could happen that someone has low heart beat and elevated blood pressure and vice versa. However, in time of exercising or anxiety the heart rate, blood pressure and cardiac output have tendency to rise. Heart stroke volume depends on the overall blood volume and actual blood supply demand to deliver nutrients and oxygen to the body. It increases in accordance with Frank-Starling law and is restricted by actual condition of the heart muscle [26]. For less trained individuals compensation of the cardiac output by raising heart rate will come earlier. Increase in cardiac output in accordance with the basic hydraulic laws will cause blood pressure raise with a gradient dependent on actual status of cardiovascular system.

2. Peripheral resistance:

Peripheral resistance depends on vessel diameter d, length- $Δ l$ , and blood viscosity- ν. The vessel diameter depends on physiological conditions and is also regulated by sympathetic nervous system [27]. Elevated blood viscosity is causing raise of resistance to blood flow resulted in surplus blood pressure. Higher vessel length related to particular body structure (height, overall weight, body mass index) is also causing raise of blood pressure. In human aorta with average diameter of d = 0.02m, blood velocity of v = 0.2 m/s and blood viscosity ν = (2.8-3.8) $10^{- 6} [\frac{m^{2}}{s}]$ , the Reynolds coefficient, Re = [ $\frac{v d}{ν}$ ] will be around 1.3*10³ indicating laminar flow. However the flow was found transient and turbulent in aorta due to local vessel configuration [27]. In case the friction factor is considered according to well-known relation:

α = \frac{64}{Re}

Linear pressure drop through the aorta or other blood vessel can be expressed by Darcy-Weisbach formula [28]:

Δ P = α \frac{v^{2}}{2 g} \frac{Δ l}{d} = \frac{64}{R e} \frac{v^{2}}{2 g} \frac{Δ l}{d} = 32 \frac{v \times ν}{g d^{2}} Δ l

Where v is blood velocity, g is the gravitational constant,

Δ l

is the vessel length and

ν

is the blood viscosity.

Assuming the pressure drop through any of the closed body vessel loops is identical to the systolic pressure then it can be described as follows:

P_{s} = 32 \frac{ν}{g} \sum_{j = 1}^{n} {(\frac{v}{d^{2}} Δ l)}_{j}

Where n is the number of vessel sections in the body loop. Equation (6) shows strong dependence of P_s on the vessel diameter which could be permanently reduced due to aging and disease (local or overall restriction) or temporary in condition of anxiety.

3. Vessel elasticity

Vessel elasticity (Young's modulus -E) is a property of the tissue. PWV and Young's modulus are changing with age (A) [13, 29]. It was shown that Young's modulus increases by 50% toward the age of 30 [30]. In case of atherosclerosis the vessel become calcified and more rigid loosing possibility to absorb blood pressure variations during heart cycle. Moreover the vessel diameter decreases and this leads to rise in the blood pressure [31, 32]. Assuming Young's modulus and destressed diameter have horizontal asymptotic tendency depending on age, we could approximate it as follows:

E (A) = \frac{E_{m} A^{k} + E_{0}}{A^{k} + 1}

d (A) = R \frac{(d_{0} - 2 h) {(A - A_{0})}^{k_{1}} + d_{0}}{{(A - A_{0})}^{k_{1}} + 1}

where E₀ is the initial Young's modulus (in infant); E_m is the maximum Young's modulus of calcified vessel of aged individual; k and k₁ are assumed to be individual calcification indexes; d₀ is the vessel diameter of A₀ years old person ; h is the thickness of the calcified arteries layer and R is the introduced stress factor.

It should be mentioned that rigidity of the arteria and its diameter are variating in relation to the emotional status of the individual. Rigidity of the blood vessel is affecting the propagation speed of PWV created by the PP:

PP = P_{s} - P_{d}

where P_s and P_d are systolic and diastolic blood pressure.

The equation of Zukovsky determines the surge pressure in a pipe. For the applied to pressure propagation velocity in blood vessels, it could be presented as follows [33]:

PP = {ρv}_{0} PWV

where v₀ is the maximum/initial blood velocity of the heart cycle. Considering linear variation of blood velocity during the heart cycle from maximum to zero, and taking into account Eq. (3), v₀ could be expressed as follows:

v_{0} = 2 \frac{Q}{S} = \frac{8 \times SV \times HR}{{πd}^{2}}

where Q is the blood flow and S is the arteria cross sectional area. Using Eqs. (1), (10) and (11) the PP is:

PP = \frac{8 ρ \times SV \times HR}{{πd}^{2}} \sqrt{\frac{Eh}{dρ}}

Equation (12) shows that PP could be estimated and monitored by simultaneous measurement of heart stroke volume, heart beat rate and pressure wave velocity.

The presented measurement method, allows direct measurement of the heart beat rate. PWV is estimated by detecting the time of pressure wave propagation between heart and wrist. Stroke volume is assumed to be related to the detected amplitude of skin deformation in the heart area. It should be mentioned that PWV vary along the wave propagation path. Elasticity, diameter and thickness of blood vessels vary from ascending aorta to radial, brachial and subclavian blood vessels [27]. Therefore Eq. (12) being adapted to the three monitored parameters, could be expressed as:

PP = \frac{Δ U \times HR}{K} \times \frac{l}{t}

where

Δ U

is the recorded variation of the skin deformation amplitude; l/t is the measured average PWV between the heart and the wrist ; HR – recorded heart rate ; K – calibration coefficient obtained during testing of the particular person, which includes influence of the vessel diameter.

The measured time of pressure wave propagation on a distance l between the heart and wrist could be expressed as follows:

t = \int_{0}^{l} \frac{dx}{PWV (x)} = \int_{0}^{l} \frac{dx}{\sqrt{\frac{E (x) h (x)}{d (x) ρ}}}

The PWV component in the introduced measurement could be divided into three relatively constant parts - aorta, artery and arteriole, each have different distance ( $l$ ), diameter (d), wall thickness (h) and elasticity (E). The total time of the wave propagation from the heart to the hand is:

t_{T o t a l} = \frac{l_{1}}{{PWV}_{1}} + \frac{l_{2}}{{PWV}_{2}} + \frac{l_{3}}{{PWV}_{3}} = (\frac{l_{1}}{\sqrt{\frac{E_{1} h_{1}}{d_{1}}}} + \frac{l_{2}}{\sqrt{\frac{E_{2} h_{2}}{d_{2}}}} + \frac{l_{3}}{\sqrt{\frac{E_{3} h_{3}}{d_{3}}}}) {(ρ)}^{1 / 2}

In Table 1 below we present an approximation for the lumen caliber (D) and wall thickness (h) of each vessel [27]. Those values can be used in the presented mathematical model.

Table 1. Approximate lumen caliber (D) and wall thickness (h) of each vessel [27].

View Table

The presented novel, easy, comfortable and low cost method offers to have an initial and individual calibration and measurement of HR, PWV and skin vibration amplitude in order to predict continuous changes in PP and BP.

3. Experimental setup

Sketch of the constructed setup is presented on Fig. 1. The experimental set up configuration is given in Fig. 2.

Fig. 1 Implemented optical configuration for remote measurement of blood pressure from a subject: sketch of the optical system.

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Fig. 2 Implemented optical configuration for remote measurement of blood pressure from a subject: subject's hand and heart under lasers illumination as viewed by the cameras.

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The setup includes two green lasers to illuminate the wrist and heart areas (to generate the secondary reflected speckle patterns), and two defocused cameras which were connected to a computer for processing the information.

The pressure waveforms were recorded in the two mentioned sites (heart and wrist). First measurement was taken during rest. Additional measurements were taken after certain physical effort until initial BP was obtained. During the test, BP measurements were taken using a blood pressure cuff. For each tested individual, the distance between the two measurement sites was estimated. Age, weight, height and gender were also recorded.

Average transit time of the pressure wave, was calculated from the time delay between the two corresponding peaks. PWV average was calculated according to Eq. (2). HR average was calculated by measuring time between relevant peaks for both heart and hand waveforms graphs. Pulse pressure was calculated according to Eq. (8), based on the parallel recorded systolic and diastolic pressure. Average amplitude variation was calculated for each waveform graph.

4. Experimental results

Results of measurements presented in Fig. 2 were recorded and analyzed. Example for Ballistocardiogram (BCG) recording is given in Fig. 3, Fig. 4 and Fig. 5.

Fig. 3 Waveform propagation measurement from heart area.

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Fig. 4 Waveform propagation measurement from the wrist.

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Fig. 5 Waveform propagation measurement from the wrist and hand.

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Figure 5 shows a time delay related to pulse wave propagation between two measurement sites. Values of the first peaks of each heart beat were marked and PWV average and HR were calculated according to Eq. (2). This was our computation since we found that by this method, the propagation time of the wave could easily be estimated.

As described above, BP was measured and the pulse pressure was determined for each measurement in order to evaluate its connection to PWV. Results obtained from individuals aged from 25 to 70 are presented in Fig. 6.

Fig. 6 Pulse Wave Velocity via Pulse Pressure of four individuals.

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Figures 6 shows that PWV variation is different for each person and depends on individual physical condition.

However, it can be noticed that in each graph the first part is constant and the second part contains a main slope. PWV increases with PP, due to blood vessels elasticity effect until a critical point when the compensatory effect of the blood vessels reaches the limit. After this point the rise of the PP causes sharp increase in PWV. In an elderly individual tested, the PWV was raised three times reaching a value of around 16 m/s. For young and healthy individuals the PWV variated in the range of 6 to 10 m/s which corresponds to the results of other researchers based on representable PWV measurements [24, 25]. Please note that the graphs of Fig. 6 are not used for the calibration procedure. The calibration is to be obtained by initial recording of PP and BP. Also note that the experimentally recorded wavefronts have strong signal with period around 0.2s. We assume that the 0.2s period is related to the properties of the vascular system including propagation wave reflection on the arterial brunches.

5. Conclusions

In this paper, pulse waves propagations were recorded using a novel technique based on tracking the temporal changes in the position and in the amplitude of reflected secondary speckle patterns produced in human skin when illuminated by a laser beam. Simultaneous recordings and measurements in two areas (heart and wrist) allowed measuring PWV continuously.

Relation between pulse pressure and PWV before and after exercise was demonstrated to determine possibility for blood pressure monitoring.

The proposed technique can be used for remote noncontact blood pulse wave velocity and pressure measurement by using the mathematical model described in the paper. Initial calibrations would be needed in order to find the right proportion coefficient for each individual (coefficient K introduced in Eq. (13)). Calibration will be obtained by initial recording of PP and BP by the non-invasive and low cost method described in this paper.

References and links

1. R. Asmar, A. Benetos, J. Topouchian, P. Laurent, B. Pannier, A. M. Brisac, R. Target, and B. I. Levy, “Assesment of Arterial Distensibility by Automatic Pulse Wave Velocity Measurement,” Hypertension 26, 485–490 (1995). [CrossRef] [PubMed]

2. J. Blacher, R. Asmar, S. Djane, G. M. London, and M. E. Safar, “Aortic Pulse Wave Velocity as a Marker of Cardiovascular Risk in Hypertensive Patients,” Hypertension 33(5), 1111–1117 (1999). [CrossRef] [PubMed]

3. S. J. Vermeersch, E. R. Rietzschel, M. L. De Buyzere, D. De Bacquer, G. De Backer, L. M. Van Bortel, T. C. Gillebert, P. R. Verdonck, and P. Segers, “Age and gender related patterns in carotid-femoral PWV and carotid and femoral stiffness in a large healthy, middle-aged population,” J. Hypertens. 26(7), 1411–1419 (2008). [CrossRef] [PubMed]

4. T. G. Pickering, J. E. Hall, L. J. Appel, B. E. Falkner, J. Graves, M. N. Hill, D. W. Jones, T. Kurtz, S. G. Sheps, and E. J. Roccella, “Recommendations for blood pressure measurement in humans and experimental animals: Part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research,” Hypertension 45(1), 142–161 (2005). [CrossRef] [PubMed]

5. G. Mancia, G. Bertinieri, G. Grassi, G. Parati, G. Pomidossi, A. Ferrari, L. Gregorini, and A. Zanchetti, “Effects of blood-pressure measurement by the doctor on patient’s blood pressure and heart rate,” Lancet 332(8352), 695–698 (1983). [CrossRef] [PubMed]

6. R. P. Lifton, A. G Gharavi, and D. S Geller, “Molecular Mechanisms Review of Human Hypertension,” Cell 104(4), 545–556 (2001). [CrossRef] [PubMed]

7. L. Peter, N. Noury, and M. Cerny, “A review of methods for non-invasive and continuous blood pressure monitoring: Pulse transit time method is promising?” IRBM 35(5), 271–282 (2014). [CrossRef]

8. B. Gribbin, A. Steptoe, and P. Sleight, “Pulse Wave Velocity as a Measure of Blood Pressure Change,” Psychophysiology 13(1), 86–90 (1976). [CrossRef] [PubMed]

9. B. P. Imholz, J. J. Settels, A. H. van der Meiracker, K. H. Wesseling, and W. Wieling, “Non-invasive continuous finger blood pressure measurement during orthostatic stress compared to intra-arterial pressure,” Cardiovasc. Res. 24(3), 214–221 (1990). [CrossRef] [PubMed]

10. J. Fortin, W. Marte, R. Grüllenberger, A. Hacker, W. Habenbacher, A. Heller, Ch. Wagner, P. Wach, and F. Skrabal, “Continuous non-invasive blood pressure monitoring using concentrically interlocking control loops,” Comput. Biol. Med. 36(9), 941–957 (2006). [CrossRef] [PubMed]

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

12. S. Yoshimura, J. Sugai, H. Hashimoto, T. Okamura, N. Yamagishi, M. Hasegawa, T. Hayashi, F. Otsuka, E. Ishikawa, H. Yamashita, and M. Kozakai, “An estimation of arteriosclerosis by the measurement of pulse wave velocity and an analysis of the clinical effect of therapeutic agents on arteriosclerosis,” Cor Vasa 10(3), 173–182 (1968). [PubMed]

13. P. Lantelme, C. Mestre, M. Lievre, A. Gressard, and H. Milon, “Heart Rate: An Important Confounder of Pulse Wave Velocity Assessment,” Hypertension 39(6), 1083–1087 (2002). [CrossRef] [PubMed]

14. S. S. Najjar, A. Scuteri, V. Shetty, J. G. Wright, D. C. Muller, J. L. Fleg, H. P. Spurgeon, L. Ferrucci, and E. G. Lakatta, “Pulse Wave Velocity Is an Independent Predictor of the Longitudinal Increase in Systolic Blood Pressure and of Incident Hypertension in the Baltimore Longitudinal Study of Aging,” J. Am. Coll. Cardiol. 51(14), 1377–1383 (2008). [CrossRef] [PubMed]

15. T. Pereira, C. Correia, and J. Cardoso, “Novel Methods for Pulse Wave Velocity Measurement,” J. Med. Biol. Eng. 35(5), 555–565 (2015). [CrossRef] [PubMed]

16. Y. Beiderman, I. Horovitz, N. Burshtein, M. Teicher, J. Garcia, V. Mico, and Z. Zalevsky, “Remote estimation of blood pulse pressure via temporal tracking of reflected secondary speckles pattern,” J. Biomed. Opt. 15(6), 061707 (2010). [CrossRef] [PubMed]

17. K. Meigas, R. Kattai, and J. Lass, “Continuous blood pressure monitoring using pulse wave delay,” in Proceedings of IEEE Conference on Engineering in medicine and biology society (IEEE, 2001), pp. 3171–3174. [CrossRef]

18. A. A. Kamshilin, S. Miridonov, V. Teplov, R. Saarenheimo, and E. Nippolainen, “Photoplethysmographic imaging of high spatial resolution,” Biomed. Opt. Express 2(4), 996–1006 (2011). [CrossRef] [PubMed]

19. V. Teplov, E. Nippolainen, A. A. Makarenko, R. Giniatullin, and A. A. Kamshilin, “Ambiguity of mapping the relative phase of blood pulsations,” Biomed. Opt. Express 5(9), 3123–3139 (2014). [CrossRef] [PubMed]

20. Z. Zalevsky, Y. Beiderman, I. Margalit, S. Gingold, M. Teicher, V. Mico, and J. Garcia, “Simultaneous remote extraction of multiple speech sources and heart beats from secondary speckles pattern,” Opt. Express 17(24), 21566–21580 (2009). [CrossRef] [PubMed]

21. V. V. Tuchin, “Coherent optical techniques for the analysis of tissue structure and dynamics,” J. Biomed. Opt. 4(1), 106–124 (1999). [CrossRef] [PubMed]

22. S. S. U. I’yanov, D. A. Zimnyakov, and V. V. Tuchin, “Fundamentals and applications of dynamic speckles induced by focused laser beam scattering,” Opt. Eng. 33(10), 3189–3210 (1994). [CrossRef]

23. E. D. Lehmann, “Clinical value of aortic pulse-wave velocity measurement,” Lancet 354(9178), 528–529 (1999). [CrossRef] [PubMed]

24. M. Kikuya, T. Ohkubo, H. Metoki, K. Asayama, A. Hara, T. Obara, R. Inoue, H. Hoshi, J. Hashimoto, K. Totsune, H. Satoh, and Y. Imai, “Day-by-Day Variability of Blood Pressure and Heart Rate at Home as a Novel Predictor of Prognosis: the Ohasama Study,” Hypertension 52(6), 1045–1050 (2008). [CrossRef] [PubMed]

25. G. de Simone, M. J. Roman, M. J. Koren, G. A. Mensah, A. Ganau, and R. B. Devereux, “Stroke Volume/Pulse Pressure Ratio and Cardiovascular Risk in Arterial Hypertension,” Hypertension 33(3), 800–805 (1999). [CrossRef] [PubMed]

26. J. P. Konhilas, T. C. Irving, and P. P. de Tombe, “Frank-Starling law of the heart and the cellular mechanisms of length-dependent activation,” Pflugers Arch. 445(3), 305–310 (2002). [CrossRef] [PubMed]

27. J. A. Arimon, Numerical Modelling of Pulse Wave Propagation in the Cardiovascular System: Development, Validation and Clinical Applications (Imperial College London, 2006).

28. P. G. Kiselev, A. D. Aldshul, N. V. Danilchenko, A. A. Kasparson, G. I. Krivchenko, N. N. Pashkov, and S. M. Slissky, Hydraulic Calculations (Moscow, 1974).

29. H. Tomiyama, A. Yamashina, T. Arai, K. Hirose, Y. Koji, T. Chikamori, S. Hori, Y. Yamamoto, N. Doba, and S. Hinohara, “Influences of age and gender on results of noninvasive brachial-ankle pulse wave velocity measurement--a survey of 12517 subjects,” Atherosclerosis 166(2), 303–309 (2003). [CrossRef] [PubMed]

30. P. G. Agache, C. Monneur, J. L. Leveque, and J. De Rigal, “Mechanical properties and Young’s modulus of human skin in vivo,” Arch. Dermatol. Res. 269(3), 221–232 (1980). [CrossRef] [PubMed]

31. A. C. Pearson, R. Guo, D. A. Orsinelli, P. F. Binkley, and T. J. Pasierski, “Transesophageal echocardiographic assessment of the effects of age, gender, and hypertension on thoracic aortic wall size, thickness, and stiffness,” Am. Heart J. 128(2), 344–351 (1994). [CrossRef] [PubMed]

32. T. Länne, B. Sonesson, D. Bergqvist, H. Bengtsson, and D. Gustafsson, “Diameter and Compliance in the Male Human Abdominal Aorta: Influence of Age and Aortic Aneurysm,” Eur. J. Vasc. Surg. 6(2), 178–184 (1992). [CrossRef] [PubMed]

33. T. M. Bashte, Hydraulic Machines and Drives (Moscow, 1970).

34. K. Sutton-Tyrrell, S. S. Najjar, R. M. Boudreau, L. Venkitachalam, V. Kupelian, E. M. Simonsick, R. Havlik, E. G. Lakatta, H. Spurgeon, S. Kritchevsky, M. Pahor, D. Bauer, A. Newman, and Health ABC Study, “Elevated aortic pulse wave velocity, a marker of arterial stiffness, predicts cardiovascular events in well-functioning older adults,” Circulation 111(25), 3384–3390 (2005). [CrossRef] [PubMed]

Vessel	Aorta	Arteries	Arterioles	Capillaries	Venules	Veins	Vena cava
Number	1	10²	10⁷	10¹⁰	10⁹	10²	1
D (mm)	25	4	0.03	0.008	0.02	5	30
h (mm)	2	1	0.02	0.001	0.002	0.5	1.5

Monitoring blood vital bio signs using secondary speckle patterns

Abstract

1. Introduction

2. Theoretical background

1. Cardiac output

2. Peripheral resistance:

3. Vessel elasticity

3. Experimental setup

4. Experimental results

5. Conclusions

References and links

Cited By

Figures (6)

Tables (1)

Equations (15)

Optics Express