IEEE SENSORS JOURNAL, VOL. 6, NO. 3, JUNE 2006 819

Method for Continuous Nondisturbing Monitoringof Blood Pressure by Magnetoelastic Skin

Curvature Sensor and ECGEugenijus Kaniusas, Helmut Pfützner, Member, IEEE, Lars Mehnen, Jürgen Kosel, Juan Carlos Téllez-Blanco,

Giedrius Varoneckas, Audrius Alonderis, Turgut Meydan, Manuel Vázquez, Michael Rohn,Alberto M. Merlo, and Bernd Marquardt

Abstract—This paper concerns continuous nondisturbing es-timation of blood pressure using mechanical plethysmographyin connection with standard electrocardiography (ECG). Theplethysmography is given by a novel magnetoelastic skin cur-vature sensor (SC-sensor) applied on the neck over the carotidartery. The sensor consists of a magnetoelastic bilayer partlyenclosed by a coil. Bending the bilayer causes large changes ofmagnetic permeability which can be measured by the coil. TheSC-sensor signal and the ECG signal are adaptively processedin order to estimate blood pressure according to a specificallyestablished theoretical model. The model uses estimated vesselradius changes and pulse transit time as parameters. The resultsshow cross correlation coefficients in the range 0.8 up to 0.9between reference and estimated values of systolic blood pressure,diastolic blood pressure, and systolic/diastolic blood pressurechange, whereas the estimation error was below 4 + 7 mmHgat rest and increased with the stress level. Limitations of themodel applicability are given by a hysteretic behavior of bothmodel parameters due to inert changes in artery stiffness. TheSC-sensor and the ECG electrodes cause minimal inconvenience tothe patient and offer an approach for a continuous nondisturbingmonitoring of blood pressure changes, as being relevant for sleepmonitoring or biomechanic feedback.

Index Terms—Blood pressure, electrocardiography, magnetoe-lastic amorphous ribbons, mechanical plethysmography, physio-logical sensors, skin curvature sensor.

Manuscript received April 30, 2005; revised July 14, 2005. This workwas supported by the Hochschuljubiläumsstiftung Vienna and the EU projectB-SENS (G5RD-CT-2002-00690). The associate editor coordinating thereview of this paper and approving it for publication was Prof. Eugenii Katz.

E. Kaniusas, H. Pfützner, L. Mehnen, J. Kosel, and J. C. Téllez-Blancoare with the Institute of Fundamentals and Theory of Electrical Engineering,E351, Bioelectricity and Magnetism Lab, University of Technology, A-1040Vienna, Austria (e-mail: kaniusas@tuwien.ac.at; pfutzner@tuwien.ac.at;mehnen@tuwien.ac.at; juergen@tuwien.ac.at; carlos.tellez@uibk.ac.at).

G. Varoneckas and A. Alonderis are with the Institute of Psychophysiologyand Rehabilitation, Kaunas University of Medicine, LT-00135 Palanga,Lithuania (e-mail: giedvar@ktl.mii.lt; audriusa@ktl.mil.lt).

T. Meydan is with the Wolfson Centre for Magnetics Technology,School of Engineering, Cardiff University, Cardiff CF24 0YF, U.K. (e-mail:meydan@cf.ac.uk).

M. Vázquez is with the Instituto de Ciencia de Materiales de Madrid, CSIC,Campus de Cantoblanco, 28049 Madrid, Spain (e-mail: mvazquez@icmm.csic.es).

M. Rohn is with the Profactor Produktionsforschungs GmbH, A-4407 Steyr/Gleink, Austria (e-mail: michael.rohn@profactor.at).

A. M. Merlo is with the Centro Ricerche Fiat, Cabin Systems and HMIDepartment, Vehicle Development, 50-10043 Orbassano, Italy (e-mail: alber-tomaria.merlo@crf.it).

B. Marquardt is with the ELCAT Medical Systems, 82515 Wolfratshausen,Germany (e-mail: bernd-marquardt@elcat.de).

Digital Object Identifier 10.1109/JSEN.2006.874438

I. INTRODUCTION

CONTINUOUS noninvasive monitoring of blood pressurein humans is relevant in many medical areas as sleep

monitoring or biomechanic feedback. The accurate and nondis-turbing monitoring during the large variety of stressors is a chal-lenge that many methods have attempted to solve.

As is well known, noninvasive standard methods are basedon an inflatable pressure cuff surrounding an arm or a finger. Inmost cases, they are restricted to a periodic, intermittent estima-tion of at discrete time intervals, e.g., of 1 min. As a drawback,recurrent inflating and deflating of the cuff stress the patient. Pe-riodic interrupts of the blood flow affect also the physiologicalstate of interest and disturb the quality of sleep.

Consequently, many attempts were made to develop noninva-sive methods of continuous nature. One approach is to decreasethe degree of applied compression to the actual systolic value,evaluating about 15 beats [1]. Another approach is based on thevolume clamp principle as described in [2]. Here, pressure isapplied to the finger and adjusted in an adaptive way in order tobalance the instantaneous value of as a function of time. How-ever, these methods involve inconvenience to the patient due topressure application, and they tend to affect the physiologicalstate of interest.

An attractive approach to avoid inconvenience is to useimpedance plethysmography, which reflects volumetric and/orconductivity changes associated with the cardiovascular circle;the registered waveform is used to estimate [3]. Anotherwell-known nondisturbing method takes advantage from thepositive correlation between pressure and the pulse wavevelocity , i.e., velocity of propagating blood pressure waves[4]. The evaluation of can be based on the determination ofthe pulse wave transit time , i.e., the time interval between theR-wave of an electrocardiography (ECG) signal and the pulsewave maximum as detectable by means of a plethysmographicsensor at the finger [5]. However, its reliability is restricted dueto the fact that a priori complex behavior of is approximatedby a single parameter using a simple mathematical modelbased on rough approximations.

This paper describes a novel concept of the nondisturbingmonitoring of , which utilizes , and—as a novelty—an addi-tional direct detection of mechanical oscillations of the carotidartery wall. The oscillations are registered using a novel skincurvature sensor (SC-sensor) which consists of a bending sen-sitive magnetoelastic bilayer partly enclosed by a coil for signal

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820 IEEE SENSORS JOURNAL, VOL. 6, NO. 3, JUNE 2006

Fig. 1. (a) Setup for the skin curvature sensor including a bilayer and a coil. Change of blood pressure p from its diastolic value p to its systolic value p inducesperiodic changes of artery radius r from its diastolic value r to its systolic value r . (b) The curvature c of the bilayer increases (�c / �r) during the systole(p = p ) and a cardiac component s (= s) of the skin curvature signal s is induced. In addition to s , the respiratory component s is present due to neckcircumference changes. Ultrasonic pictures on the right demonstrate r and r for p and p , respectively.

establishment [6] [Fig. 1(a)]. Earlier, the SC-sensor had beensuccessfully applied for the registration of local changes of theskin curvature yielding cardiorespiratory activity on the neck[7], cardiac acceleration on the chest [8], and other physiologicparameters [9]. As an advantage, the SC-sensor and ECG elec-trodes avoid any inconvenience to the patient because no activeapplication of the pressure is involved and offer two independentparameters, i.e., and oscillations of the carotid artery wall forthe estimation of .

As an alternative, the assessment of the oscillations ofthe carotid artery wall could be performed by means of animpedance plethysmography signal which, however, shows adisadvantageous cross sensitivity to changes of tissue conduc-tivity. Furthermore, the SC-sensor allows more local assessmentof the curvature than the impedance plethysmography as ap-plied in [3] and, thus, offers a more accurate measure of theoscillations.

Within the scope of the introduced concept, is calculatedusing the time shift between the skin curvature signal of theSC-sensor and the ECG signal . The adaptive processingof and offers the oscillations of the carotid arterywall and a basis for a more reliable monitoring of .

II. METHODS

A. Applied Model

The pulsatile blood flow during the cardiovascular circle is di-rectly connected with the oscillations of between its systolicvalue and its diastolic value . The corresponding pressuredifference , as well as the absolute valuesand are generally assumed to represent important physio-logic parameters.

Earlier, the aforementioned model [4], [5] for the contin-uous estimation of and utilized the pulse wave velocity

Fig. 2. Application of the skin curvature sensor on the neck over the carotidartery at distance L from the heart. Propagation of blood pressure p waves (sys-tolic value p ) is shown, � and v being the propagation time and pulse wavevelocity of blood pressure waves, respectively. The ECG signal s is de-rived according to Einthoven II.

—as an inverse measure for —for the very heterogeneousvessel path between the cardiac region and the finger. Here,we present an advanced model which considers over a morehomogeneous path between the heart and the neck, distal ar-teries not being involved. As already mentioned, in addition to

, the model considers a second novel parameter, i.e., periodicchanges of the vessel radius (Fig. 1) as arising during thecardiovascular circle. The model estimates , , and foreach heart beat.

1) Estimation of Systolic/Diastolic Pressure Change: Themodel makes use of the SC-sensor applied at the neck abovethe carotid artery corresponding to Fig. 2. The detection of

KANIUSAS et al.: METHOD FOR CONTINUOUS NONDISTURBING MONITORING OF BLOOD PRESSURE 821

is based on the fact that a pressure increase yields a radiusincrease according to [10]

(1)

Here, is the module of volume elasticity of the vessel. Equa-tion (1) assumes axisymmetric and radial deformation of ar-teries and constant length of the artery; the constant length canbe assumed due to very high modulus of elasticity in the longi-tudinal direction as well as constrained and prestressed state ofthe arteries in vivo [11]. Furthermore, (1) neglects viscoelasticnature of the wall [12]. As an approximation, we assume that

corresponds to the diastolic radius [Fig. 1(a)] which is as-sumed to be independent from due to lacking inward disten-sion of the arterial wall [11].

Furthermore, we assume that the artery is located on muscleof rigid constant geometry, variations due to respiratory ac-tivity being considered through adaptive signal filtering (seeSection II-B). This means that corresponding to a sys-tolic-diastolic radius increase ( systolicradius) yields a displacement of the skin surface at the sensor’scentral region by a distance [ in Fig. 1(b)].The quantity considers “damping” of displacement,especially due to a possible layer of fat tissue. In approximation,this displacement yields a proportional increase of the sensorcurvature and, thus, a systolic-diastolic increase of thesensor signal component which reflects the cardiac activityonly. Neglecting several sources of nonlinearity, we can assumethat a radius increase yields an increase . Then, (1) canbe given as

(2)

Here, the new constant considers , , as well as indi-vidual characteristics of the SC-sensor.

In principle, (2) would offer a basis for the detection of .However, as is well known, is constant only for low valuesof [10]. It increases with increasing due to nonlinearlyincreasing stiffness of arteries. An attractive possibility to con-sider this increase is to use the dependence of on which,according to [10], is given through

(3)

where is the blood density which can be assumed to be nearlyconstant. The values of can be estimated by evaluating thecorresponding , as shown in Fig. 2. Here, is thedistance between the cardiac region and the neck.

The total of this procedure yields an equation which is similarto the Bramwell and Hill equation, to give

(4)

Equation (4) shows that can be estimated using the ratio. If we assume to be constant during the cardiovas-

cular cycle and apply the transitions and

, we get an estimation for with as a arbitrary cor-rection constant, to give

(5)

The constants and can be calculated by solving (5) fortwo calibration values of .

2) Estimation of Systolic Pressure and Diastolic Pressure:Equation (4) would offer also a basis for a numerical estimationof and if we would substitute the difference quotientsthrough the differential quotients and numerically integrate theresulting equation. However, it is practically impossible to es-timate and more than once within the cardiac cycle, i.e.,within the single transition of from to , and vice versa.

In order to estimate and , we assume—in addition to theaforementioned assumptions of the artery deformation—thatthe external forces are reduced to stresses acting only in theradial direction of the artery wall and the thickness of theartery wall is much smaller than . Applying the Laplace’s law[12], [13], the circumferential tensile stress is

(6)

Here, is the excess pressure, i.e., the radius of the vesselis at (zero transmural pressure), is the Young’smodulus in circumferential direction and , are the Poissonratios in the longitudinal and circumferential direction. We as-sume that the artery wall is incompressible, so that

.On the other hand, can be expressed by means of the

Moens–Kortweg equation [12]

(7)

and the solution for from (6) is

(8)

Furthermore, we assume that yields a small and nearly con-stant which does not depend on the value of ,as assumed also in (1). Then, for the estimation of , (8) yields

(9)

For the estimation of , the above assumption yields equalitiesand and (8) can be rewritten as

(10)

The values of in (9), (10), and in (10) are estimatedaccording to the considerations in Section II-A1, the constant

822 IEEE SENSORS JOURNAL, VOL. 6, NO. 3, JUNE 2006

Fig. 3. Signal flow diagram for estimation of pressure difference p , systolic blood pressure p , and diastolic blood pressure p , using the skin curvature signals and ECG signal s .

in (10) is an arbitrary correction constant. The constants andcan be calculated by solving (9) for two calibration values of, whereas and can be calculated by solving (10) with

already estimated (9) and two calibration values of .

B. Model Parameter Calculation

Fig. 3 shows the signal flow diagram for the estimations ofmodel parameters and (5), (9), (10) from andin order to calculate , , and .

Generally, on the neck shows a complex nature. A res-piratory component is induced by the neck circumferencechanges due to breathing [7]. In addition, a cardiac component

is induced by the blood pressure waves, as discussed above.In the following, we will use for , in accordance with theuse of in Section II-A.

As shown in Fig. 3, was extracted out of by meansof an adaptive highpass filter. The cutoff frequency

Hz of the finite impulse response (FIR) filter (length 1082)was adjusted after each pulse beat according to the actual heartrate derived from the corresponding R-R interval of .In addition, a narrow band cardiac component was extractedby means of an adaptive bandpass FIR filter (length 3098, band-width 0.2 Hz) whose middle frequency was set to actual aftereach pulse beat. For the visualization of , an adaptive low-pass FIR filter (length 1082) was applied to . The cutofffrequency Hz of the filter was adjusted after eachpulse beat, analogous to the adaptive highpass filter.

In general, could be detected by means of a secondSC-sensor on the neck, in some centimeters distance fromthe first one. However, the resulting values in our experimentalprestudies proved to be small, in the order of a few milliseconds,which impede an effective resolution. Alternatively, couldbe assessed by a second SC sensor in the cardiac region butthis method proved to be problematic due to strong respiratoryartefacts.

We evaluate the much higher value which arises betweenthe cardiac region and the neck, the distance being of the orderof 30 cm for adult persons. For this purpose, we registerand determine between corresponding instants of time withrespect to the cardiac component .

As shown in Fig. 3, the value of was calculated for eachheart beat in two different ways. The systolic transit time was

estimated as the time delay between the R-wave of and themaximum of after the R-wave. As a second method, the meantransit time was estimated as a time delay between and

using cross correlation in between. In principle, a crosscorrelation between and would also offer a basis for theestimation of . However, it proved to be difficult to interpretthe resulting peaks of the correlation result due to wide bandnature of and due to the dicrotic notch in [Fig. 4(b)]. Theparameter for (5) and (10) was calculated for each heartbeat as root mean square value of within the correspondingheart beat interval (Fig. 3).

C. Experimental

The signal was recorded by a SC-sensor which was at-tached on the neck over the carotid artery (Fig. 2) by a double-sided adhesive tape. The signal was derived accordingto Einthoven II. The sample rate of and was 500Hz. Subjects were five healthy male test persons. Changes of

were induced by veloergometry, i.e., by cyclic load applica-tion on a veloergometer. The persons were asked to perform thefollowing exercise sequence: 60 s resting without bicycling, 60s bicycling with 150 W load, 300 s resting, 60 s bicycling with150 W load, and 120 s resting. A total of eight experiments werecarried out.

The reference signals for , , and were establishedby continuous noninvasive blood pressure monitor POR-TAPRES (Finapres Medical Systems) which is based on avolume clamp principle using two inflatable pressure cuffs onfingers (see Section I). The ultrasonic pictures in Fig. 1 wererecorded by an echocardiograph (Megas, Esaote Biomedica) inparallel to the veloergometry.

III. RESULTS

A. General Observations

Significant insights into the physiologic interpretation ofcan be derived from its course over the cardiac cycle. Fig. 4compares the measured waves of in the aorta and the radialartery with the wave of . It can be observed that the similarity isastonishing between the waves of [Fig. 4(a)] and [Fig. 4(b)].All important qualitative characteristics of can be found in, i.e., strong wave increase at the beginning of the systole, a

KANIUSAS et al.: METHOD FOR CONTINUOUS NONDISTURBING MONITORING OF BLOOD PRESSURE 823

Fig. 4. Comparison of measured blood pressure p in catheterized patients [14] with cardiac component s (= s) of the skin curvature signal over a single cardiaccycle. (a) Radial wave of p shows a higher amplitude and a more pronounced dicrotic notch (due to reflections and increased artery stiffness [12]) than simultane-ously recorded aortic wave of p. (b) Wave of s being more similar to the radial wave.

Fig. 5. Adaptive filtering of skin curvature signal s . (a) Superimposed car-diac and respiratory components oscillating with cardiac rate f and respira-tory rate f , respectively. (b) Respiratory component s extracted by an adap-tive lowpass filter. (c) Wide band cardiac component s (= s) extracted by anadaptive highpass filter. (d) Narrow band cardiac component s extracted by anadaptive bandpass filter.

little dicrotic notch, and a slow decrease during the diastole. Inparticular, the radial wave of shows a very strong similarityto the wave of ; the reason for this similarity may be the factthat is assessed over the carotid artery which is not proximal,as given for the radial artery either. These observations on thecardiac wave suggest that the waves of directly represent thewaves of .

Fig. 5 shows results on the adaptive filtering (Fig. 3) in orderto extract and out of the “mixed” signal .As shown in Fig. 5(a), exhibits two clearly recognisable

signal components, the one oscillating with and the secondslower one oscillating with the respiratory rate .

The output signal [Fig. 5(c)] of the adaptive highpass filter isa wide band signal oscillating with and exhibiting basic andhigher cardiac harmonic. The output [Fig. 5(d)] of the adap-tive bandpass, in contrast, is a narrow band signal including onlythe basic cardiac harmonic. In addition, Fig. 5(b) demonstrates

for comparison, which was extracted by the adaptive low-pass. It can be observed that the extracted signals , , andmatch visually well the corresponding components in the orig-inal signal [Fig. 5(a)].

The estimation of the model parameters and isdemonstrated in Fig. 6 showing and for two differentvalues of . Comparing Fig. 6(a) mmHg withFig. 6(b) , it can be observed that with increasing

the value of increases significantly, whereasdecreases. The observed qualitative changes of and forincreasing are in full agreement with the model in (4),which implies an increase of both and with increasing .

The estimated values of and were investigated with re-spect to accuracy and robustness. As expected, were moreirregular due to presence of higher cardiac harmonic within[Fig. 5(c)]. However, were more precise than because theestimation of includes some indirect averaging effects overthe cardiac cycle, i.e., the averaging due to the sinusoidal shapeof [Fig. 5(d)] and due to the application of the cross correla-tion function. Thus, was considered to be more appropriatefor the applied model—compare confirming results for and

in Table I—since depends on in a strong and a nonlinearway (5), (9), (10).

Fig. 7 shows the estimated model parameters andalong with during veloergometry. The recurrent bicy-cling temporally increases [Fig. 7(a)] and decreases[Fig. 7(b)]. The observed changes of and for varyingvalues of [Fig. 7(c)] qualitatively match the observed be-havior of the parameters in Fig. 6. In the given case, the crosscorrelation coefficient between and the reference is0.82, whereas the coefficient of 0.83 between and the refer-ence is negative, as can be also deduced from Figs. 6 and 7.

824 IEEE SENSORS JOURNAL, VOL. 6, NO. 3, JUNE 2006

Fig. 6. ECG signal s and cardiac component s of skin curvature signal for two different blood pressure changes. (a) p = 47 mmHg and (b) p =

76 mmHg. For increasing p , an increase of curvature signal deflection s and a decrease of systolic propagation time � can be observed.

TABLE IMEAN, ABSOLUTE VALUES OF CROSS CORRELATION COEFFICIENTS

Closer investigations of and over varying showeda hysteretic behavior, as demonstrated in Fig. 8. The values of[Fig. 8(a)] exhibit 10%–20% higher values for an increase of

than for its decrease . Furthermore,the hysteresis loop of shows much higher width, revealinghigher values of for . Both hysteresis indicatethe tendency of a delayed decrease of stiffness for ,i.e., shorter and lower for increased levels of stiffness.

B. Comparison With The Reference Blood Pressure

Fig. 7(c) shows the estimated and reference during velo-ergometry. For the estimation of , the constants and

(5) were calculated using one calibration point at 0 s andthe second one at 110 s [marked by circles in Fig. 7(c)]. It canbe observed that the reference and estimated are in goodagreement, showing nearly identical values at rest, an overesti-mation during bicycling (Table II) and a high correlation coeffi-cient of about 0.94. It is important to note that the correlation ofthe model output with the reference is improved in com-

parison with the correlation of the corresponding single modelparameters or with the reference (Section III-A),which shows the efficiency of the model.

Fig. 9 demonstrates the estimation of and in compar-ison with the corresponding reference values; the used data setis identical with the data in Fig. 7. The calibration constantsand (9) were calculated using one calibration point at 0 s andthe second one at 135 s [marked by circles in Fig. 9(b)], whereas

and (10) were calculated using calibration points at 0 sand 110 s [marked by circles in Fig. 9(a)]. It can be observedthat, in both cases, the estimation fits well the respective ref-erence, the correlation coefficients being 0.93 and 0.92 forand , respectively. Analogous to the estimation of , thereference and estimated values of are similar at rest whilean overestimation occurs during bicycling. In the case of , aslight underestimation can be observed (Table II).

Table I summarizes mean, absolute values of cross correla-tion coefficients for the estimation of , , and . The cor-relation values between the single parameters and the reference

KANIUSAS et al.: METHOD FOR CONTINUOUS NONDISTURBING MONITORING OF BLOOD PRESSURE 825

Fig. 7. (a) Curvature signal deflection s during veloergometry. (b) Estimated systolic propagation time � . (c) Estimated and reference values of pressuredifference p showing a cross correlation coefficient around 0.94.

Fig. 8. (a) Hysteretic behavior of estimated systolic propagation time � over the reference systolic blood pressure p . The analyzed segments of � and p areshown in Fig. 7(b) and Fig. 9(a), respectively, for the time range extending from 373 to 568 s. (b) Corresponding hysteresis loop of curvature signal deflection s

from Fig. 7(a) over p .

values indicate that is more decisive for than for (0.9for versus 0.73 for ), whereas for , the reverse is true(0.42 versus 0.74). This behavior proves the assumption for thediastolic model that is nearly constant, thus (9) yieldingonly one parameter . The comparison of and shows that

seems to be more decisive for the estimation of all three refer-ence signals, which confirms the aforementioned behavior ofand (Section III-A). The outputs of the models (9), (10) forthe estimation of and seem to be effective when comparedto the estimations using the single parameters, the correlation co-

efficients being about 0.86 and 0.92 for and , respectively.The output of the model for the estimation of (5) shows thesame effectiveness, as given by the single parameter .

The absolute estimation errors for , , and are givenin Table II. It can be observed that the mean value of the estima-tion error for increases from 4 mmHg at rest to 24 mmHgduring exercise, the averaged error being about 8 mmHg. Sim-ilar errors can be observed for , whereas the errors for arenegative (mean values) and are much lower, for instance, the av-erage error is only about 2 mmHg.

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TABLE IIESTIMATION ERROR OF ABSOLUTE VALUES

Fig. 9. (a) Estimated and reference systolic blood pressure p , showing a mutual cross correlation coefficient of about 0.93. (b) Estimated and reference diastolicblood pressure p with cross correlation coefficient of about 0.92.

IV. DISCUSSION

Within this study a simple model (5), (9), (10) was devel-oped for the estimation of , , and . Two physiologicalparameters serve as model input, and . The first param-eter has already been shown to be inversely correlated with

[4], [5] because of propagating blood pressure waves in-creases with increasing [15]; however, the hysteretic behaviorwas not investigated in neither of the above studies. The secondparameter , in contrast, is novel in its direct experimentalassessment by the use of the novel magnetoelastic SC-sensor.The results show that reflects and increases with in-creasing because the outward distention of the arterial wallincreases. It is important to note that the changes of withinthe cardiac cycle induce a hysteretic behavior of [16], [17];however, we have been unable to find any objective data on thehysteretic behavior during the long term changes of , for in-stance, during veloergometry.

The results demonstrate the importance of the parameterand for the estimation of and , respectively. The com-bination of both parameters seems to be effective for the esti-mation of and , especially in the case of , whereas forthe estimation of the effectiveness is the same as using thesingle parameter (Table I).

As a novel finding, both parameters and show hys-teretic behavior over the long term changes of , which, ob-viously, is a disadvantage for the model. The hysteresis maybe caused by a physiological phenomenon, i.e., inert changesof the stiffness [proportional to in (1)] of vessels for varying

. The stiffness is regulated by strengthening of surroundingplain muscles; during an increase of the stiffening processis in progress, whereas during the subsequent decrease ofthe value of decreases slowly with some delay, as indicatedby studies involving stress [12], [15]. As a result, the values of

are shorter for the decrease of because of the delayed de-crease of and, thus, the delayed increase of . Analogous, thelower values of result for the decrease of because of thedelayed decrease of .

In addition, the hysteretic alterations of the viscoelastic arteryproperties, as shown in [12] and [15], may also have an influenceon the observed hysteretic behavior of the parameters. Further-more, the dynamic viscoelastic properties depend on whichwas also observed to show a hysteretic behavior. During the in-crease of , the values of were lower for given than duringthe decrease of . The lower and higher values of tend to re-duce and increase [12], respectively, this tendency qualitativelysupporting the observed hysteretic behavior of and .

KANIUSAS et al.: METHOD FOR CONTINUOUS NONDISTURBING MONITORING OF BLOOD PRESSURE 827

As shown in Table II, the absolute values of and showmuch lower estimation errors at rest than during bicycling, i.e.,increased stress levels (4 mmHg at rest versus 24 mmHg at ex-ercise). In contrast, the bicycling deteriorates the estimation of

to a lesser extent ( 0.8 versus 3.7 mmHg). This differ-ence may be attributed to the fact that the model parameteris only relevant for the estimation of and (5), (10), butnot of (9), for is a mechanical quantity which can beunfavorably influenced by the body movements accompanyingbicycling.

Furthermore, the behavior of and [Figs. 7(c) and 9(a)]shows that during the first period of bicycling the reference andestimated values are in good agreement, as can be expected fromthe calibration point within this period. However, an interestingphenomena can be observed during the second period of bicy-cling, showing that the estimated values and are higherby about 20% than the respective reference values and the cor-responding estimated values during the first period of bicycling.This overestimation is obviously due to the increased values ofthe model parameter (5), (10) during the second period ofbicycling, as compared to the first period of bicycling [Fig. 7(a)];the other model parameter shows similar values at its valleysduring both periods of bicycling. The increased values ofmay be attributed to the facts that during the second period ofbicycling 1) already more blood is in peripheral circulation, 2)the tension of the vessel smooth muscles is different due to auto-nomic control, which alters the stiffness of the artery walls [12],[15], and 3) the artery walls after the first period of bicycling andthe subsequent resting period are more relaxed, e.g., due to de-layed stress relaxation, and of the vessels is decreased. Asdecreases, increases by analogy with the discussed inter-pretation of the hysteretic behavior of .

It is very interesting to observe in Figs. 7 and 9 that the valleysof and reference are slightly delayed in comparison withthe peaks of and reference . This observation (and thedata in Table I) supports the deduced model for the estimationof (9), which uses only as parameter, for the valleys ofthe estimated show an analogous delay as the reference .Analogous, the relevance of for the estimation of can bederived out of the concomitance of the corresponding peaks (andthe data in Table I), which is considered through the model forthe estimation of (10) using both and as parameters.

The discussed hysteretic behavior of the model parametersas well as the delayed restoration of of the vessels impairthe estimation of , , and , especially during rapid andshort period changes of stress levels. Furthermore, an accurateestimation of by the SC-sensor is restricted to persons withnormal, not excessively adipose neck. In the case of the adiposeneck, the changes of the skin curvature due to the cardiac activityare too low in order to establish a reliable signal ( foradipose neck, see Section II-A1).

It is important to note that the results of this study might havebeen biased by 1) the frequency dependence of the artery vis-coelastic properties, i.e., the dependence of artery stiffness onthe instant value of [15], 2) the restricted reliability of POR-TAPRES, in particular, during increased stress levels [18], [19]as applied in the present study, 3) the strong expected variabilityof due to the duration variability of the isovolumic ventricles

contraction, since the distance is rather short and ECG signalis used as reference [20], and 4) the missing assessment of thediastolic to be used for the estimation of in addition to thesystolic which should be preferably used only for the estima-tion of .

Summing up, the proposed approach for the noninvasivecontinuous monitoring of blood pressure offers the followingimprovements:

1) Apart from considering the transit time, the model con-siders changes of vessel radius in a nondisturbing way.

2) The estimated transit time is more reliable than in the caseof the distal application of sensors. This is due to a nearlyhomogenous vessel system over the evaluated relativelyshort distance heart-neck. The distance does not involvesignificant local increases of blood pressure, as given byincreasing stiffness of distal arteries and pulse wave reflec-tions from distal arteries and arterioles.

3) Application of the developed mathematical model is moreappropriate within the distance heart-neck because frictionis marginally involved here.

The results indicate that the developed method using thenovel SC-sensor and standard ECG electrodes can be appliedfor the qualitative assessment of the blood pressure, i.e., forthe blood pressure changes. The quantitative assessment showslow errors at rest, however, during exercise the quantitativeassessment is restricted by the hysteretic behavior of the modelparameters. The applied sensors are easy to handle and causeminimal inconvenience to the patient, as being relevant formany medical areas where blood pressure changes are ofinterest, e.g., sleep monitoring, stress ECG registration, biome-chanic feedback.

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Eugenijus Kaniusas was born in Siauliai, Lithuania,in 1972. He received the degree in control and au-tomation engineering and the Dr.Tech. degree inauscultation and processing of acoustic soundsfrom the Faculty of Electrical Engineering andInformation Technology, Vienna University ofTechnology (VUT), Vienna, Austria, in 1997 and2001, respectively.

Since 1997, he has been with the Institute ofFundamentals and Theory of Electrical Engineering,Bioelectricity and Magnetism Lab, VUT. His re-

search interests include electric, acoustic, optic, and magnetoelastic sensors forbiomedical applications, signal processing, as well as digital and analogoushardware concepts for signal acquisition.

Helmut Pfützner (M’79) was born in Salzburg, Austria. He received the Ph.D.degree from Vienna University of Technology (VUT), Vienna, Austria, and the“venia docendi” for applied fundamentals of electrical engineering in 1979 and1983, respectively.

Since 1972, he has been with the Institute of Fundamentals and Theory ofElectrical Engineering, VUT, where he has been Head of the Institute’s Bio-electricity and Magnetism Laboratory since 1985. He is engaged in research,consulting, and teaching in several fields of biophysics and magnetism.

Lars Mehnen was born in Munich, Germany, in 1967. He received the Dr.Tech.degree from the Faculty of Computer Science, Vienna University of Technology,Vienna, Austria, in 1997 and 2002, respectively.

His research interests include electric and magnetoelastic sensors for biomed-ical applications, simulations, evolutionary strategies/optimization methods, aswell as digital hardware control concepts.

Jürgen Kosel was born in Wels, Austria, in 1977. He received the Dipl.Ing.degree from the Vienna University of Technology (VUT), Vienna, Austria, in2002.

Since 2002, he has been with the Institute of Fundamentals and Theory ofElectrical Engineering, VUT. His research interests include magnetostrictivesensors for biomedical and automotive applications.

Juan Carlos Téllez-Blanco was born in Havana, Cuba, in 1964. He gradu-ated from the Faculty of Physics, Havana University, in 1990, and received theDr.Tech. degree from the Institute of Experimental Physics, Vienna Universityof Technology, Vienna, Austria, in 2000.

His research interests include technical physics and solid state physics.

Giedrius Varoneckas is a Professor, Chief Research Associate, and Head ofthe Department of Psychosomatic Disturbances and Sleep Research, Institute ofPsychophysiology and Rehabilitation, Kaunas University of Medicine, Palanga,Lithuania. His research activities cover the psychophysiology of cardiovascularsystem, sleep, application of computerized heart rate variability analysis, oper-ator functional state assessment, and the treatment of sleep disorders.

Audrius Alonderis is aJunior Researcher and Head of the Sleep Research Lab-oratory, Department of Psychosomatic Disturbances and Sleep Research, Insti-tute of Psychophysiology and Rehabilitation, Kaunas University of Medicine,Palanga, Lithuania. His main research activities are in the fields of sleep researchand autonomous control of the cardiovascular function, as well as the evaluationof the psychoemotional status.

Turgut Meydan, photograph and biography not available at the time of publi-cation.

Manuel Vázquez born in Madrid, Spain. He received the Ph.D. degree inPhysics from the University of Madrid in 1980.

He has been a Professor of Research at the Institute for Material Sciencesof Madrid, Spanish Council for Research, since 1996. The topics of hisscientific research include amorphopus and nanocrysalline solids, micro- andnanowires, magnetoelasticity and magnetostriction, magnetization reversal,micromagnetism.

Michael Rohn was born in 1968. After studying technical physics at the Uni-versity of Linz, Linz, Austria, and environment protection technologies at theVienna University of Technology, Vienna, Austria, he began to focus on phys-ical measurement techniques in the field of radiation protection. Since 1999, hehas been working in the field of nanoscale metrology at Profactor Produktions-forschungs GmbH, Steyr/Gleink, Austria.

Alberto M. Merlo started his professional activity at the Centro Ricerche FIAT,Italy, in 1993. Since 2004, he has been working in the field of car interior design,particularly with the application of advanced microsensors for the monitoringof the cabin environment, with a special focus on driver status and passengercomfort.

Bernd Marquardt studied telecommunications at the University College ofMunich, Munich, Germany.

Since 1981, he has been the General Manager and Companion of ELCATMedical Systems GmbH, which is a medium-sized company developing andproducing various electronic devices for vascular diagnostics and other medicalfields.