Methods of the Pulse Wave Velocity Estimation based on Pneumatic

Blood Pressure Sensor Data and Synchronous ECG Records

V. E. Antsiperov

1 a

, G. K. Mansurov

and A. S. Bugaev

Kotelnikov Institute of Radioengineering and Electronics of RAS, Mokhovaya 11-7, Moscow, Russian Federation

Moscow Institute of Physics and Technology, Institutskiy per. 9, Dolgoprudny, Russian Federation

Keywords:

Non-invasive Arterial Blood Pressure Monitoring, Pneumatic Sensor, Pulse Wave Transit Time, Pulse Wave

Velocity, ECG Synchronization, Atherosclerosis Diagnosis.

Abstract:

The article discusses a new method for diagnosing atherosclerosis with the help of a pneumatic blood pressure

sensor recently developed by the authors. Since atherosclerosis is a progressive disease characterized by

the deposition of cholesterol and certain fractions of lipoproteins on the walls of blood vessels, it is always

accompanied by an increase in stiffness of the artery walls. One technique to assess arterial stiffness, is the

measurement of arterial pulse wave velocity, that is the distance traveled by blood ﬂow divided by the time it

takes to travel that distance. So the direct method for estimating pulse wave velocity is to measure the transit

time of a pulse wave between a pair of artery points by means, for example, of any tonometric sensors. In

this connection the paper discusses the possibility of using a new type of sensors developed by the authors

—– pneumatic sensors —- to measure the pulse wave transit time. However, given the existing features of

these sensors and, accordingly, the special features of pressure measurements, it was necessary to signiﬁcantly

modify the direct method for estimating pulse wave velocity. The main modiﬁcation characterizing the new,

indirect method consists in evaluating the delay of the pulse wave at the points of the artery with respect

to some characteristic moment of a synchronous ECG (e.g. the time moment of R–peak, that corresponds

to heart ventricles contractions). The details of this method and its modiﬁcation in the form of a simpliﬁed

single–point method of estimating pulse wave velocity form the main content of the work.

1 INTRODUCTION

Today, cardiovascular disease is the main cause of the

deterioration in the health of many people, including

mortality, worldwide. In its turn, the most common

cause of cardiovascular diseases is atherosclerosis, a

progressive disorder leading to the build-up of choles-

terol and other lipoprotein fractions on artery walls.

Such a buildup is characterized by the formation of

atherosclerotic plaques followed by wall calciﬁcation,

which leads to artery deformation and narroing (up

to occlusion). As this reduces the ﬂow of oxygen-

saturated blood to vital organs (e.g. heart and brain), it

causes the myocardial and cerebral ischemia. As a re-

sult, the risk of such severe disorders as acute myocar-

dial infarction or stroke (caused by thrombosis upon

the occlusion) increases signiﬁcantly (Laurent et al.,

2006). For this reason now, the research and develop-

ment of reliable methods for detecting atherosclerosis

https://orcid.org/0000-0002-6770-1317

in early stages (subclinically) is an extremely impor-

tant problem.

Atherosclerosis can be detected using damage

markers of internal organs (e.g. heart, kidneys, arter-

ies) during the invasive study. However, it is clear that

non–invasive methods seem more appealing in terms

of diagnosing atherosclerosis in the subclinical phase.

Essentially, those methods are related to structural

and functional assessment of the blood vessel state.

In this context we must mention (A) Doppler ultra-

sonography used to evaluate the intima media thick-

ness and analyze plaques, as well as the (B) ankle-

brachial index evaluation method employed to iden-

tify peripheral artery disorders. Besides the afore-

mentioned methods, today’s medicine emphasizes a

group of methods based on analyzing systolic blood

pressure and, more generally, the shape of the blood

pressure pulse wave. Largely, this trend can be ex-

plained by the discovery of the relationship between

blood pressure parameters and arterial stiffness, with

the latter depending on the calciﬁcation of walls.

Antsiperov, V., Mansurov, G. and Bugaev, A.

Methods of the Pulse Wave Velocity Estimation based on Pneumatic Blood Pressure Sensor Data and Synchronous ECG Records.

DOI: 10.5220/0009155303010307

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS, pages 301-307

ISBN: 978-989-758-398-8; ISSN: 2184-4305

301

2 ASSESSMENT OF ARTERIAL

WALL STIFFNESS BY PWV

ESTIMATION

One of the most prominent parameters (the gold stan-

dard) for the assessment of arterial stiffness is the

pulse wave velocity (PWV), i.e. the speed of the pres-

sure pulse wave (PW) propagating along the walls of

the artery caused by the release of blood ﬂow, ejected

by the heart during the systole (Evangelista, 2011).

So, the direct PWV measurement method is under-

pinned by estimating the PW transit time T

pwtt

− T

, i.e. the time for which the wave travels be-

tween two different points at the artery. In its turn,

PWV will be the distance d travelled by wave divided

by transit time: V = d/T

pwtt

. Any pair of tonomet-

ric sensors can be used for the implementation of this

method, provided that those sensors are located prox-

imally above the surface arteries, distally against the

heart (on the carotid, femoral, radial, and other arter-

ies) (Kim and Kim, 2019), see Figure 1.

Figure 1: Tonometric sensors locations for PWV estima-

tion. PWV V is calculated by dividing the distance d be-

tween the sensors by measured PW transit time T

pwtt

To date, there are several types of tonometric sen-

sors employed for estimating PWV under the above

method. They may differ by the mode of action (from

applanation tonometry, oscillometric approach, etc.

to magnetic tomography) and by design. The detailed

review of known direct PWV measurement methods

employing various sensors and results derived here

can be found in (Cavalcante and Lima, 2011), (Bene-

tos et al., 2010). Typical reference values of PWV

for people without signiﬁcant cardiovascular prob-

lems that are currently used as guidelines for assess-

ing the state of blood vessels can be found in (Benetos

et al., 2010), see also Table 1.

Recently, the authors of this work also proposed

to use a new type of sensors – pneumatic sensors for

continuous arterial blood pressure monitoring in the

problem of the pulse wave velocity estimation. How-

Table 1: PWV by age groups of patients in norm (1455 per-

sons) – adapted from (Benetos et al., 2010).

Age Mean PWV Median PWV

(year) (±2SD) (10-90 pc)

< 30 6.2 (4.7-–7.6) 6.1 (5.3-–7.1)

30—39 6.5 (3.8—9.2) 6.4 (5.2-–8.0)

40—49 7.2 (4.6-–9.8) 6.9 (5.9-–8.6)

50—59 8.3 (4.5—12.1) 8.1 (6.3-–10.0)

60—69 10.3 (5.5-–15.0) 9.7 (7.9-–13.1)

≥ 70 10.9 (5.5-–16.3) 10.6 (8.0-–14.6)

SD abbriviation is for standard deviation;

10 pc is upper limit (10%); 90 pc is

lower limit (90%).

ever, in view of the existing features of these sensors

and, accordingly, peculiarities in measurement meth-

ods, we had to signiﬁcantly modify the direct method

for estimating PWV. The main change characterizing

the new, developed by us method consists in mea-

suring indirectly the transfer time between a pair of

points at the artery by measuring PW delays for both

points in relation to the R–peaks of the ECG record

(time moments of the heart ventricles contraction).

The details of this method and the results obtained are

presented below in the paper.

3 USING PNEUMATIC BLOOD

PRESSURE SENSORS IN PWV

ESTIMATION

Some time ago the authors of this paper suggested a

new method of non–invasive continuous arterial blood

pressure measurement that employed the principle

of local compensation and represented the advance-

ment of applanation tonometry methods. In techni-

cal terms the principle is realized in the form of a

pneumatic sensor (Antsiperov and Mansurov, 2019a),

(Mansurov and Antsiperov, 2017), Figure 2.

From some perspective, the pneumatic sensor op-

erates as a pressure relief valve (PRV). When the mea-

suring unit’s outlet is covered by soft tissues of the

wrist, the continuous air ﬂow from the receiver in

pneumatic circuit coming into the chamber increases

pressure P

sen

in it. But, when P

sen

slightly exceeds

the artery wall pressure P

art

, a part of the air leaks out

due to elastic displacement of surface tissues, and that

aligns P

sen

to P

art

, see Figure 2. However, it should be

noted that there is one signiﬁcant difference between

the sensor and an ordinary PRV. While a pressure drop

is a one–time event for a PRV during its normal op-

eration, the sensor operates in conditions of perma-

BIOSIGNALS 2020 - 13th International Conference on Bio-inspired Systems and Signal Processing

302

sen

art

Pronator Quadratus

Ulnar Bone

Radius Bone

Radial

Artery

Figure 2: (A) the sketch of the pneumatic sensor’s measur-

ing unit (on the radial artery), (B) the sensor design includ-

ing the electronic circuit with measuring unit (bottom) and

pneumatic circuit (top), (C) location of the measuring unit

on the patient’s wrist during ABP monitoring.

nent air leakage, continuously tracking the changes

of blood pressure: P

sen

∼ P

art

Besides their main purpose (monitoring time–

dependent blood pressure dynamics P

art

(t)), the sen-

sors described can be used as tonometers that reg-

ister time parameters of PW (e.g. moments of sys-

tolic blood pressure values, pressure fronts, PW feet,

etc.). Recently we have carried out corresponding ex-

periments aimed at direct measurment of PWV with

the help of blood pressure sensors as tonometers. A

small group of volunteers (12 people) participated in

these experiments, and a set of methods was used, that

differed slightly in criteria (selection of characteristic

points of the PW waveform) used to estimate the tran-

sit time. The results turned out to be consistent with

the reference data given in Table 1.

However, the experiments also showed that the

use of pneumatic sensors in the direct PWV measur-

ment method is a scrupulous and complex procedure

requiring ﬁne and barely controllable measurements.

The key reason for this is the problem of the proper

positioning of pneumatic sensors – the issue we have

already mentioned earlier (Antsiperov and Mansurov,

2019a), (Antsiperov and Mansurov, 2019b). In fact,

to implement the local compensation method, the

measuring unit’s outlet has to be very small (less than

1 mm in diameter, while the preloaded radial artery is

3 mm in diameter). This said, to ensure proper mea-

surements (P

sen

∼ P

art

), the outlet has to be positioned

accurately above the artery’s central axis (accurate to

fractions of a millimetre). And as long as this artery

is unobservable, the outlet is positioned recurrently,

based on the signal waveform. As a result, the pro-

cedure of positioning the measuring unit turns out to

be adaptive to the registered signal –— this means

the blood pressure may change in unclear ways. If

there are two sensors employed and they are located

in different places on the artery (see Figure 1), the

synchronous measurement with the concurrent mon-

itoring of positions of both sensors becomes a hard–

going task, accompanied by an abrupt decrease in the

measurement stability on both channels.

4 INDIRECT METHOD FOR PWV

ESTIMATION

The problem of measuring transit time with the help

of pneumatic sensors was solved by virtue of apply-

ing an indirect method. Basically, this method im-

plies carrying out measurements for two points at dif-

ferent time moments – in this case, only one sensor

will be needed. This concept may be implemented

the following reasons. Let us assume that the delay

of PW is measured not between the selected points

but in each of those points against another event (e.g.

against time T

of ventricular contraction, i.e. posi-

tion of R–peak of the ECG). Denoting delays by δ

and δ

respectively, it would be true that T

= T

+ δ

and T

= T

+ δ

. This said, the PW transit time be-

tween the selected points is T

pwtt

= T

−T

= δ

−δ

Now, if delays δ

and δ

depend only on geometri-

cal and physical properties of a respective artery and

do not depend on dynamically changing environment,

we do not have to assess them against the same T

. To

this end, δ

and δ

can be assessed against the events

happening at different times, with the help of the same

(the only) sensor.

To assess the possibility of realizing the afore-

mentioned indirect PWV measurement method, our

pneumatic sensor (Antsiperov and Mansurov, 2019a),

(Mansurov and Antsiperov, 2017) was equipped with

an additional channel for synchronous ECG signal

processing. As the purpose of such a modiﬁcation is

relating time parameters of PW to the heart’s rhyth-

mic activity, ECG was implemented in the form of a

very simpliﬁed single-channel ampliﬁer based on the

original dry electrode (without conductive gel) circuit

without a neutral electrode.

Figure 3 shows the fragment of a 12–second syn-

chronous dynamics of blood pressure signals P

sen

(t)

measured with a modiﬁed pneumatic sensor on the

patient’s wrist (Figure 2 C) and ECG signal V

ecg

(t)

from wrists of both hands. In Figure 3 on both sig-

nals the time markers (vertical lines) denote locations

on time axis of R-peaks T

(i)

, i = 1,2,... and PW feet

(i)

, usually considered as beginnings of respective

pulses. One can see with the naked eye that upon the

explicit variability of R—-R intervals (T

(i+1)

− T

(i)

)

and f–f intervals (T

(i+1)

− T

(i)

), the delay periods

of PW against R–peak — pulse propagation times

(i)

ppt

= T

(i)

− T

(i)

remain mostly the same.

The table 2 presents quantitative data on the vari-

ability of the R–R and f–f intervals, as well as the

mean pulse propagation time and its standard devia-

tion for 12 volunteers. As usual, variability can be

estimated in any way by comparing the mean val-

Methods of the Pulse Wave Velocity Estimation based on Pneumatic Blood Pressure Sensor Data and Synchronous ECG Records

303

Table 2: Temporal parameters of synchronously measured blood pressure (wrist) and ECG (leads on the wrists) signals.

No Age Health Num of Mean R–R SD R–R Mean f–f SD f–f Mean R–f SD R–f

(year) status averag. (sec) (sec) (sec) (sec) (sec) (sec)

1 60 infarct 35 0.8591 0.0077 0.8599 0.0101 0.1109 0.0038

2 25 healthy 34 0.9279 0.0357 0.9252 0.0343 0.1425 0.0039

3 27 healthy 43 1.0320 0.0236 1.0325 0.0240 0.1345 0.0023

4 50 healthy 50 0.9858 0.0384 0.9850 0.0407 0.1151 0.0045

5 62 tachyc. 48 0.8119 0.0065 0.8117 0.0061 0.1100 0.0020

6 56 hypert. 84 0.8575 0.0168 0.8572 0.0148 0.1103 0.0039

7 57 healthy 52 0.8222 0.0114 0.8215 0.0148 0.1174 0.0065

8 22 arrhyt. 22 1.0595 0.0435 1.0569 0.0434 0.1558 0.0026

9 22 arrhyt. 23 1.0506 0.0799 1.0532 0.0460 0.1374 0.0297

10 30 healthy 27 0.7250 0.0236 0.7259 0.0252 0.1311 0.0048

11 80 infarct 16 0.8648 0.0110 0.8633 0.0152 0.1100 0.0073

12 61 healthy 22 0.9125 0.0179 0.9067 0.0205 0.1060 0.0060

Mean and SD (standard deviation) were calculated after trimming 10 % of the lowest values

and 10 % of the highest values (10-90 pc).

120

110

100

20 22 24 26 28 3230

t, sec

Psen, mmHg

Vecg, con.u





Tptt

Figure 3: Synchronous measurement of (A) blood pressure and (B) ECG signals. Vertical lines denote moments of R–peaks

and corresponding PW feet. The ﬁgure shows that even upon the variability of R—R and foot–foot intervals, pulse propagation

time T

ppt

= T

− T

remains very stable.

ues (Mean) and standard deviations (SD) displayed

in adjacent columns of the table for the correspond-

ing intervals. To obtain the Mean and SD values

the averaging was carried out by the number of car-

diocycles (column Num of averag.) in each record

made in a separate experiment (for a speciﬁc volun-

teer at a speciﬁc location on the artery of the measur-

ing unit). These experiments were carried out using a

single pneumatic sensor as a tonometer with its ﬁxed

locations on the radial and brachial arteries in 12 vol-

unteers of different ages and health conditions. The

measurement data obtained for one of two locations

on the radial artery (on the patient’s wrist) are given

in Table 2 (Figure 3 shows the fragment of volun-

teer No 1’s data). Eventually, the experiment results

turned out to be consistent with the data obtained dur-

ing the implementation of the direct method. To this

end, they are consistent with the data given in Table

1. With that, the indirect method proved to be more

convenient and less demanding than the direct method

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304

(as expected). Also, the measurement of PWV with

the help of the indirect method does not require any

special training. Instead, the operator only needs to

follow simple instructions. We think that after im-

proving the method in compliance with existing med-

ical requirements, the developed PWV measurement

method can be used in clinical practice.

The experiments turned out to give an unexpected

result – an opportunity to measure PWV with the help

of one pneumatic sensor located at only one point. In

essence, it impies that one imaginary sensor is located

next to the aortic arch and the other – a real sensor – is

located in the place, where the easiest measurement of

blood pressure can be provided, e.g. above the radial

artery, as illustrated in Figure 4 A. We should note that

this concept is hardly innovative. There is already an

accepted methodology for measuring PWV with the

help of a combination of the ECG and a plethysmo-

graphic sensor mounted on the patient’s ﬁnger. For

details and references to similar methods, please refer

(Bereksi-Reguig et al., 2017), (Castro et al., 2017),

(Proenca et al., 2010), (Liu et al., 2011), (Oreggia

et al., 2015). However, it is worth noting that although

our method is similar to the well-known plethysmo-

graphic approaches, it interprets the measurement of

the transit time T

pwtt

, used to calculate PWV, some-

what differently (see Figure 4 B).

aort output

Vecg



Tptt

art radial

Tpep

Tpwtt

aort output

art radial

jugular cavity

aortic arch

subclavian

artery

brachial

artery

ulnar artery

radial

artery

Vecg

ppt

-T

pep

)



Figure 4: PWV estimation with only one sensor located at

only one point. (A) imaginary sensor location above the

aortic arch and real sensor location on the wrist. (B) illus-

tration of relations between ECG V

ecg

(t), and blood PWs

aortout put

(t), P

artradial

(t) at locations shown in (A).

For exfmple, following their colleagues, the au-

thors of (Bereksi-Reguig et al., 2017) are guided by

the suggestion that the moment when R–peak appears

in the ECG almost matches the moment when the

pulse wave foot starts forming at the aorta output /

at the beginning of the subclavian artery. With that,

the pulse wave transition time T

pwtt

can be taken as

the delay period T

ptt

– pulse transit time (see Fig-

ure 4 B). After that, by dividing d (distance from the

jugular cavity to the ﬁnder tip) by the delay period,

PWV will be measured as V = d/T

ptt

. And though

(Bereksi-Reguig et al., 2017) mentions that T

pwtt

dif-

fers from T

ptt

by the pre-ejection period T

pep

(time

between ventricular contraction and beginning of the

formation of the pulse wave at the aorta output, see

Figure 4), the authors consider that difference negli-

gible (T

pep

≪ T

ptt

5 PULSE TRANSIT TIME

CORRECTION

However, our study showed that when the blood pres-

sure sensor is installed on the wrist or higher on the

arm, the suggestion T

pep

≪ T

ptt

becomes very in-

accurate or even incorrect. In fact, the authors of

the recent study (Kortekaas and van Velzen, 2018)

provided results of echocardiographic T

pep

measure-

ments for three groups, 20 persons in each, namely:

focus groups with no cardiovascular disorders under

50 years (A), older than 50 years (B), and having car-

diovascular risk factors (hypertension, dyslipidemia,

kidney failure, and diabetes) (C). The results derived

in (Kortekaas and van Velzen, 2018) can be summa-

rized in the following way: T

pep

for groups (A) and

(B) lies in ranges 58.5 ± 13.0 msec and 52.4 ± 11.9

msec, respectively; for (C) within the range 57.6 ±

11.6 msec. By comparing these results with our data

shown in Table 2, where T

ptt

taken as “Mean R–f” for

similar groups comes to 140.5 ± 10.1 msec for (A),

112.1 ± 4.8 msec for (B) and 110.4 ± 5.0 msec for

(C), it is not too hard to ﬁgure up that T

pep

represents

a considerable part of T

ptt

(up to half of the latter) in

all groups. In this regard, it is interesting to note that

the aim of the study (Kortekaas and van Velzen, 2018)

was to substantiate the non–obvious assumption, as

we show above, that T

pep

≪ T

ptt

For the foregoing reasons, PWV measurement

should employ the correct formula V = d/(T

ptt

−

pep

) that contains the pre-ejection period T

pep

. For-

tunately, it is not necessary to calculate T

pep

in ev-

ery case. The abovementioned study (Kortekaas and

van Velzen, 2018) states that T

pep

has weak individual

variations, at least within speciﬁc groups (A, B, C) of

patients. This said, having the reference values T

pep

for the corresponding groups and determining the af-

ﬁliation of each patient to one of these groups (for

example, from his electronic health record – EHR),

we can use the proposed method for measuring PWV

only using the pulse transit time T

ptt

, but adjusting it

using the value T

pep

for the corresponding group. The

results of estimating PWV, using the data shown in

Table2, are given in Table 3.

As seen from Table 3, the Pulse Wave Velocity ob-

tained by us with the use of the modiﬁed pneumatic

sensor following the simpliﬁed indirect PWV mea-

Methods of the Pulse Wave Velocity Estimation based on Pneumatic Blood Pressure Sensor Data and Synchronous ECG Records

305

Table 3: PWV estimation based on synchronously measured blood pressure (wrist) and ECG Signals (wrist Leads).

No Age Health Number Mean T

ptt

Refer. T

pep

Distance d PW velocity V

(year) status of averag. (sec) (sec) (m) (m/sec)

1 60 infarct 35 0.1109 0.0576 0.70 13.1

2 25 healthy 34 0.1425 0.0585 0.68 8.1

3 27 healthy 43 0.1345 0.0585 0.66 8.7

4 50 healthy 50 0.1151 0.0524 0.65 10.4

5 62 tachyc. 48 0.1100 0.0524 0.68 11.8

6 56 hypert. 84 0.1103 0.0576 0.67 12.7

7 57 healthy 52 0.1174 0.0524 0.68 10.5

8 22 arrhyt. 22 0.1558 0.0585 0.66 6.8

9 22 arrhyt. 23 0.1374 0.0585 0.66 8.6

10 30 healthy 27 0.1311 0.0585 0.68 9.4

11 80 infarct 16 0.1100 0.0576 0.66 12.6

12 61 healthy 22 0.1060 0.0576 0.64 11.9

The pulse transition time values in the column “Refer. T

pep

”, used to calculate PW velocity V ,

were chosen as centres of T

pep

ranges for groups A, B, C (see discussion above,

based on (Kortekaas and van Velzen, 2018) data), while the groups were determined

by the columns “Age” and “Health” for each volunteer.

surement method are consistent with reference val-

ues from Table 1. Moreover, they correctly reﬂect the

age trends and known correlation between PWV and

some disorders (infarct, arrhythmia). Guided by those

ﬁrst results, we can arrive at a general conclusion: de-

spite the apparent simplicity of the measurement pro-

cedure, the suggested method delivers adequate PWV

estimation results.

6 CONCLUSIONS

We can also make the following more speciﬁc and

substantial conclusions. As the suggested method im-

plies estimation of PWV in standard conditions of

blood pressure monitoring with the help of a pneu-

matic sensor (Mansurov and Antsiperov, 2017), it can

be cost–efﬁciently (by adding an ECG channel) mod-

iﬁed and transformed into a versatile system that al-

lows to estimate the stiffness of the artery walls. It

suggests preliminary judgment about the state of the

cardiovascular system, especially about the degree of

atherosclerosis development. It goes without saying

that additional costs include software augmentation

(see (Bereksi-Reguig et al., 2017)) and examination

methodology modiﬁcation (blood pressure monitor-

ing). However, we consider the cost increase negli-

gible compared to the beneﬁt delivered by the versa-

tile system itself. Realizing that this theme has much

room for studies and tests, we optimistically expect

our efforts to pay off.

ACKNOWLEDGEMENTS

The authors are grateful to the Russian Foundation for

Basic Research (RFBR), grant N 18-29-02108 mk for

the ﬁnancial support of this work.

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