Molecular and Quantum Acoustics vol. 26, (2005)
235
ACCURACY COMPARISON OF OSCILLOMETRIC AND
ELECTRONIC PALPATION BLOOD PRESSURE MEASURING
METHODS USING INTRA-ARTERIAL METHOD AS A REFERENCE
Hannu SORVOJA, Risto MYLLYLÄ, Päivi KÄRJÄ-KOSKENKARI*, Juha
KOSKENKARI**, Mauno LILJA*, Y. Antero KESÄNIEMI*
University of Oulu, Department of Electrical and Information Engineering, Optoelectronics
and Measurement Techniques Laboratory and Infotech Oulu,
PO Box 4500, FIN-90014 University of Oulu, FINLAND
*University of Oulu, Dept. of Internal Medicine and Biocenter Oulu,
90220 Oulu, FINLAND
**Dept. of Anesthesiology, University Hospital of Oulu, 90220 Oulu, FINLAND
[email protected]
This paper evaluates blood pressure measurements by the electronic palpation
method (EP) and compares their accuracy to that of the oscillometric method
(OSC) using average intra-arterial (IA) blood pressure as a reference. All of
these three measurements were made simultaneously for each patient. The EP
method, based on noninvasively detecting the amplitude of pressure pulsations in
the radial artery, differs from the ordinary palpation method by allowing also
diastolic pressure to be determined from the pulse delay produced by cuff
pressure. In one test group, measurements were conducted on healthy volunteers
in sitting and supine position during increasing and decreasing cuff pressure.
Another group, comprising older, cardiac patients, was measured only in the
supine position during cuff inflation. The results showed that the EP method was
approximately as accurate as the OSC method with the healthy subjects and
slightly more accurate with the cardiac patient group. The advantage of the EP
method is that also the wave shape and velocity of arterial pressure pulses is
available for further analysis like the assessment of arterial stiffness.
Keywords: noninvasive, blood pressure, cuff, pulse transit time, pulse wave
velocity
236
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
1. INTRODUCTION
Most noninvasive blood pressure monitors are based either on the auscultation (AUS) or
the oscillometric (OSC) method. The former relies on detecting so-called Korotkoff sounds
(automated recording or manual auscultation with a stethoscope) using decreasing cuff
pressure and is mainly used in the clinical environment. The automated oscillometric method
(OSC), in turn, is widely used in homecare monitoring, due to its ease-of-use. Both methods
are widely accepted and often referred to in literature. However, measurements performed
with devices based on these methods do not show a particularly good degree of accuracy and
repeatability, and a number of such devices are not recommended for use, as they fail to meet
the AAMI or BHS standards (ANSI/AAMI 1987, 1993, O’Brien 1990, 1993 and 2001).
The electronic palpation (EP) method was firstly introduced in 1998 (Nissilä et al
(1998), Sorvoja et al (2001, 2003 and 2004) and Vieri-Gashi et al (2000, 2001). It uses a
standard occlusion cuff around the upper arm and a wristwatch type of multi-element pressure
transducer array to sense pulsations in the radial artery. Measurements can be made both
during increasing and decreasing cuff pressure, also referred to as inflating and deflating
pressure mode in this presentation. In these measurements, diastolic blood pressure was
defined as the point where the pulse amplitude of the blood pressure signal starts to decrease,
while systolic blood pressure was defined as the last pulse detected. Diastolic blood pressure
can be defined using two fitted lines that cross at the diastolic pressure.
In the experiments described in the paper from 1998, a comparison was made between
the EP method on one hand and the OSC and AUS method on the other. These experiments
consisted of two sets of measurements. The first set used the auscultation method (15 healthy
volunteers), while the other one used the IA method as a reference (seven healthy volunteers).
In the first set, each method satisfied the criteria laid down in the AAMI standard, and
differences between the methods were small. The second set showed that the accuracy of the
EP method in systolic blood pressure measurements exceeded that of the AUS and OSC
method. Moreover, the EP method was almost as accurate as the AUS method in diastolic
blood pressure measurements, while the OSC method performed poorest.
In those measurements, the EP method determines diastolic blood pressure on the basis
of changes in the amplitude of pressure pulses. Due to reflections from peripheral arteries and
arterioles, the pressure amplitude is slightly higher in the radial than in the brachial artery.
Moreover, the amplitude discrepancy varies depending on individual vascular properties
(vasoconstriction/vasodilation), which complicates the determination of diastolic blood
pressure. If peripheral resistance and pulse wave velocity (PWV) are high, the reflected
Molecular and Quantum Acoustics vol. 26, (2005)
237
pressure pulse will augment the incident pressure pulse. Also vascular swelling can have an
effect on pulsation amplitude, because the hold-down pressure of the transducer rises.
However, cuff pressure affects not only amplitude, but also pulse transit time (PTT). As
the increasing cuff pressure level approaches the pressure level in the brachial artery, the
pulse transit time from the aorta to the radial artery is correspondingly delayed. This effect is
illustrated in Fig. 1. When the pressure produced by the cuff is less or same as the diastolic IA
blood pressure, the pulse delay is unaffected. However, as the cuff pressure increases beyond
this point, the pulse delay increases markedly and reaches its highest value at the systolic
pressure level. Thus, the maximum transit time change equals the time needed for the blood
pressure pulse to reach the systolic pressure level, i.e., go from the bottom to the top level
(Sorvoja 2001, 2003 and 2004).
re
Pressure
su
cuff pres
g
n
ti
a
l
f
n
I
∆t1
∆t2
∆t3
∆t4
∆t5
Time
Fig. 1. Time elapses as inflating cuff pressure exceeds the diastolic arterial pressure.
This pulse transit time method for defining the diastolic pressure on the basis of the
pulse delay has been reported in papers published by Geddes et al (1983), Šantić and Šaban
(1997), Šantić and Lacković (1999) and Kerola et al (1997). Geddes et al discovered the pulse
arrival time, PAT, using an occlusive cuff on anesthetised dogs and invasive pressure
measurements from the dogs’ radial arteries. The next two papers deal with diastolic blood
pressure determination in a finger, while the third paper describes both diastolic and systolic
blood pressure measurements in the brachial artery. The main problem in finger
measurements is that blood pressure in a finger is not identical to that in the brachial artery,
which is the standard measuring point. The fourth study, by Kerola et al, is impaired by the
fact that the distance between the two sensors under the cuff was too short. The result is that
when the cuff pressure exceeds the diastolic pressure, the blood volume pushing on the upper
side of the cuff produces a rocking effect, which may lead to erroneous sensor signals. In the
EP method described in this paper, the transducer is positioned on the radial artery at a
distance of about 30 cm from the cuff. Consequently, the sensor records only pressure
pulsations in the radial artery. This method of determining diastolic blood pressure has been
238
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
previously discussed by Sorvoja et al. In addition, the recorded wave shape of the pressure
pulse can be used to improve the accuracy of systolic blood pressure measurements (Sorvoja
et al 2001).
Since the EP, OSC and AUS method are all indirect means of measuring blood pressure,
based on the occlusion of blood vessels by a cuff, their accuracy may be adversely affected by
certain factors, such as the width of the cuff. Alexander et al (1977), for instance, have
demonstrated that cuff width must be a specific fraction of the subject’s arm circumference.
Thus, an inadequate cuff width produces an overestimate of pressure. Also Geddes and
Whistler (1978) have closely examined this ratio, and their results indicate that the fraction
should be at least 0.4. Moreover, the sensitivity of the cuff as a transducer is nonlinear.
Drzewiecki et al (1993-1995) used a mathematical model to describe the mechanics of the
occlusive arm cuff and concluded, among other things, that sensitivity is nonlinear at low
pressure ranges and that it plateaus when the pressure level exceeds about 130 mmHg. The
OSC method is particularly sensitive to this effect, as the cuff also functions as a pulse
oscillation sensor.
Cristalli and Ursino (1992-1994, 1996) have shown that also the viscous elastic
behaviour of soft tissue may produce a significant pressure error. In soft tissue, the pressure
applied by the cuff does not extend to the interior of the tissue. The error increases with the
distance from the centre of the cuff. Also the viscosity coefficient and the Young modulus of
the arterial wall, heart frequency and pulse amplitude have an effect on accuracy.
In the OSC method, both diastolic and systolic pressure are determined by their own
specific cuff oscillation ratios. They are obtained by measuring cuff oscillation amplitudes at
either the diastolic or systolic point divided by the maximum cuff oscillation amplitude. First,
cuff pressure is rapidly inflated above the systolic pressure level and then slowly deflated (in
about 30 seconds) to a pressure level under the diastolic pressure level. The oscillation of the
cuff achieves its maximum near the mean blood pressure. The mean arterial pressure (MAP)
can be determined at the point where the pressure oscillation of the cuff just achieves its
maximum, or, for example, ~95% of the maximum, on the diastolic side of the cuff oscillation
curve (Ursino and Cristalli 1994, 1996). Geddes et al (1981, 1983) determined the systolic and
diastolic pressure values at the oscillation amplitude levels of 0.55 and 0.82 of the maximum.
Drzewiecki et al (1994) reported the values of 0.593 and 0.717, respectively, using a
theoretical model and a constant input pressure amplitude. However, comprising only two
spectral components, their model did not consider the possible spectral diversity of the blood
pressure signal. Ursino and Cristalli (1994, 1996), in turn, used a real blood pressure pulse
wave in their mathematical study and obtained a ratio of 0.52 for systolic and 0.70 for
Molecular and Quantum Acoustics vol. 26, (2005)
239
diastolic blood pressure values. However, the ranges of the ratio deviations were quite wide,
extending from a minimum of –12% to a maximum of 23%, which introduces considerable
errors. Ursino’s model also showed the influence of arterial stiffening: both MAP and systolic
pressure error increased due to stiffening, whereas diastolic error seemed to be independent of
it. This stiffness can be described by terms like distensibility, compliance and elastic modulus,
which are difficult to measure directly. Indirect measures of arterial stiffness include pulse
wave velocity (PWV) and characteristic impedance (Asmar et al 1995 and Asmar 1999,
Blacher et al 1999, O’Rourke et al 1992, O’Rourke and Mancia 1999, Safar et al 1998).
According to Ursino’s model, the diastolic blood pressure error was mostly affected by pulse
pressure.
The purpose in this study is to assess the accuracy of the EP method and the OSC
method in the same measurements using IA blood pressure as a reference, and define the
effects of aging and apparent arterial stiffening on the accuracy of the methods (O’Rourke et
al 1992, O’Rourke and Mancia 1999, van der Hejden-Spek et al 2000, van Popele et al 2000).
We also aim at establishing the best measuring method and the best algorithm for the
determination of diastolic blood pressure in the EP method. Moreover, we shall determine
characteristic ratios for the inflating pressure mode in the OSC method, as these ratios and the
accuracy of measurements in this mode have not been reported on earlier.
2. METHODS
Two sets of measurements were carried out at the Oulu University Hospital in 1997 and
1998/99. Both studies were conducted in the intensive care unit and were approved by the
Ethical Committee of the hospital. In the first set, the test subjects were mostly young and
healthy volunteers. Parts of this research have been discussed in different conference
proceedings (Nissilä et al 1998, Sorvoja et al 2001, 2003 and Vieri-Gashi et al 2000, 2001)
and in two journals (Sorvoja et al 2003, Sorvoja and Myllylä 2004), but comparison with
OSC method measured simultaneously has not been published before. The other set studied
elderly patients who had undergone cardiac surgery (either a bypass or a valve operation, or
both). They were first measured immediately after a surgical intervention and then
remeasured the next morning, when they woke up. Tables 1 and 2 present the principal
characteristics of these two groups, including the subjects’ sex, age, height, weight, body
mass index (BMI) and average IA blood pressure value with standard deviations. The first
table shows the characteristics of the healthy volunteers and the second one those of the
cardiac patient group. Only test subjects with an arm circumference of less than or equal to 33
cm were qualified for later analyses in accordance with Geddes’ findings.
240
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
The IA blood pressure of the healthy volunteers was measured using a catheter
(Ohmeda), while their cuff pressure and radial artery pressure pulsation (EP) were measured
with a self-made blood pressure measurement device and a laptop PC with a data acquisition
card from National Instruments (DAQCardTM 700). The measurements were made twice, in
the supine and sitting position in both the inflating and deflating cuff pressure mode with a
one-minute follow-up recording period between them. The measurement device consisted of a
standard 13 cm cuff, a wristwatch-type four-channel pressure transducer array, an amplifier
unit and an automatic pressure-controlling unit. Signals were sampled at the 100 Hz
frequency. The transducer array was based on electro-thermo-mechanical film (Kirjavainen
1987, Ficher et al 1990) and was specifically designed for detecting pulsations in the radial
artery (Sorvoja et al 2005). Also Ruha et al (1996) and Ruha (1998) have reported on the use
of this kind of transducer array for biomedical applications. The amplifier unit was used to
amplify and band-pass filter cuff pressure signals (1 - 15 Hz, Butterworth filter response,
fourth order high pass, second order low pass) and signals produced by the four-element
sensor.
Tab. 1. - Study subjects (healthy volunteers).
Item
n
Age, ears
Height, cm
Weight, kg
BMI, kg/m2
Arm circumference, cm
Blood pressure measurements, n
IA systolic BP, mmHg
IA mean BP, mmHg
IA diastolic BP, mmHg
Heart rate, bpm
Men
12
26.2 ± 3.7
180.8 ± 5.6
79.4 ± 10.1
24.2 ± 2.4
31.6 ± 2.6
98
129.1± 11.2
87.3 ± 6.5
67.4 ± 5.0
66.2 ± 11.3
Women
4
30.8 ± 3.0
170.0 ± 2.4
63.5 ± 6.0
22.0 ± 1.9
28.3 ± 2.4
23
122.3 ± 10.1
83.5 ± 5.7
63.7 ± 5.3
69.8 ± 8.1
In the cardiac patient group, the measurements were based on signals produced by the
DATEXTM patient monitoring system and another, self-made, blood pressure measurement
device. In addition to the device described above, this one also comprised a microprocessor
unit for determining blood pressure. In addition, its signal band was also slightly different:
four-element transducer signals were amplified and filtered using a first-order band-pass filter
(1.7 Hz - 11 Hz) in a full custom integrated circuit. A connection from the DATEX TM device
provided an ECG signal and each patient’s radial and pulmonary intra-arterial blood pressure.
An automatic pressure-controlling unit, connected to the laptop computer, started cuff
pressure inflation and sent all pressure data wirelessly to the microprocessor unit. The
Molecular and Quantum Acoustics vol. 26, (2005)
241
microprocessor unit contained the same blood pressure detection algorithm as the laptop
computer and was designed to assess the functionality of the whole system.
Tab. 2 - Test subjects (cardiac patients). To provide a single value for each patient, the intraarterial (IA) blood pressure (BP) values given are average values of the measured IA diastolic,
mean and systolic BP values .
Item
Number
Age, years
Height, cm
Weight, kg
Body mass index, kg/m2
Arm circumference, cm
Bypass operation
Valve operation
Blood pressure measurements, n
IA systolic BP, mmHg
IA mean BP, mmHg
IA diastolic BP, mmHg
Heart rate, pulses/min
Men
14
67.8 ± 6.8
168.0 ± 8.1
71.4 ± 11.8
25.4 ± 4.4
32.3 ± 1.0
13
3
40
114.8 ± 21.3
77.8 ± 9.2
59.6 ± 6.1
87.2 ± 13.5
Women
6
65.2 ± 9.5
163.0 ± 6.6
74.1 ± 10.0
28.0 ± 4.6
32.7 ± 0.5
4
2
13
118.3 ± 19.9
79.6 ± 14.8
59.6 ± 14.5
81.5 ± 5.4
In the patient group, blood pressure measurements were made 2 - 5 times depending on
motion artifacts caused by post-operational shivering. All patients were in supine position,
and inflating cuff pressure was used. A medical doctor calibrated and flushed the catheter
(Ohmeda) and positioned it at heart level to eliminate the effects of hydrostatic pressure. Of
all measurements, 53 qualified for later analysis using averaged results, i.e., each patient was
represented by a single value. Most patients were measured in the afternoon shortly after their
operation and again in the morning, when they were fully awake and less under medication.
A. BLOOD PRESSURE DETERMINATION IN THE OSCILLOMETRIC METHOD
In this method, the cuff pressure signal is first filtered using two-second moving average
filtering, i.e., every sample is averaged 99 samples back and 100 samples forward using the
formula:
p(i) = mean[p (i - 99 : i + 100)],
(1)
where p(i) is the amplitude of the sample and i the corresponding index. The resulting
pressure matrix ignores one hundred samples from the beginning and another hundred from
the end, which has to be considered in later calculations. The filtered signal is then subtracted
from the original pressure signal yielding a high pass filtered oscillation signal with negligible
attenuation and phase distortion. This procedure allows the study of the potential effects of
analogue filtering on the oscillation curve. The moving average filtered signal serves as a cuff
pressure reference signal. To assess the influence of filtering on characteristic ratios, Fig. 2
242
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
presents both the analogically filtered (HW filtering) and the amplified oscillation signal as
well as the signal gained by subtracting the two-second moving average filtered signal (SW
filtering) from the original cuff signal. Cuff pressure was first low-pass filtered (0 - 14 Hz,
fourth order) and then HW band-pass filtered (1 - 15 Hz for the healthy group, 1.7 - 11 Hz for
the cardiac patient group) and, finally, amplified to an appropriate signal level. The lowest
SW-filtered signal was gained by subtracting the two-second moving average low-pass
filtered cuff oscillation signal from the original cuff pressure signal. The thus obtained signal
is a high-pass filtered signal with a very low cut-off high-pass frequency. The signal is then
amplified to an appropriate signal level using a software multiplier. Oscillation amplitudes are
determined by subtracting the diastolic pressure from the systolic pressure (the lower OSC
signal). Alternatively, the following lowest value can be subtracted from the highest value
(the upper OSC signal) to get an amplitude matrix with pulsation amplitude levels and the
150
IA blood pressure
Cuff pressure
100
50
0
-50
HW filtered oscillation
SW filtered oscillation
Amplitude determinations
Cuff oscillations (a.u.)
IA blood and cuff pressures (mmHg)
corresponding cuff pressures.
-100
0
5
10
Time (s)
15
20
25
Fig. 2. From top to bottom: IA blood pressure, increasing cuff pressure, analogically
hardware-filtered (HW filtering) and software-filtered (SW filtering) cuff oscillation signals
as a function of time.
The oscillation amplitude matrix can be reconstituted by multiplying the amplitude
matrix by a multiplication matrix in accordance with the formula:
k ( j) =
mean[p p (first : last )]
p p ( j)
,
(2)
where pp(j) is the IA blood pulse pressure corresponding to the oscillation pulse number j.
This method allows the reconstruction of the oscillation amplitude matrix so that it is not
Molecular and Quantum Acoustics vol. 26, (2005)
243
affected by variations in the IA pulse pressure; in other words, the pulse pressure amplitude
remains constant.
The oscillation amplitude matrix must then be filtered to eliminate the effects of motion
artifacts. Our experiments showed that three-point moving median filtering is adequate for the
purpose. Because the cuff pressure curve is not linear in neither the inflating nor the deflating
pressure mode, the oscillation matrix must be normalized. After normalization, the amplitude
matrix is multiplied by a constant to attain the maximum value of 100. Also the corresponding
pressure matrix is multiplied by a scaling multiplier such that the highest point of the
oscillation curve, or 95% of the maximum, corresponds to the average mean IA pressure. Also
Geddes et al (1983) used a similar scaling factor. As mentioned earlier, this “highest point”
should be on the diastolic side of the oscillation curve (Ursino and Cristalli, 1994). Fig. 3
shows a flowchart of all mathematical operations performed on the data to get the systolic,
mean and diastolic blood pressure values. First, cuff pressure is sensed by a DC-coupled
transducer. The signal is then amplified and band-pass filtered using both HW and SW
filtering. Amplitude matrices are subsequently obtained through specific amplitude
determination for both the HW and SW signals. The matrices are filtered using three-point
moving median filtering to eliminate casual motion artefacts. Next, by multiplying every
single pressure value by a multiplier acquired from the IA blood pressure measurement, the
matrices are normalized such that 100 % marks the highest value. In addition, the matrices are
filled to match every pressure integer value with a corresponding normalized amplitude value
(interpolation). Now, both matrices are multiplied by the previously determined scaling
multipliers so that the 95% level corresponds to the mean intra-arterial blood pressure value.
Finally, the mean diastolic and systolic blood pressure values are determined separately for
both the HW and SW filtered signals using the previously determined characteristic ratios.
244
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
C uff pre ssu re se n sor am plifie r
Hardware BP filte r (1-14 Hz )
Movin g ave rage LP filte r
O rigin al signal – filte re d signal
Pe ak
de te ction
Pe ak
de te ction
Am plitu de
calcu lation
Am plitude
calcu lation
3 points m oving
m e dian filte ring
3 points m oving
m e dian filte ring
Am plitude
n orm aliz ation
Am plitu de
n orm aliz ation
Pre ssu re
inte rpolation
Pre ssu re
inte rpolation
Pre ssu re
n orm aliz ation
Pre ssu re
n orm aliz ation
Me an blood pre ssure de te rm ination
Diastolic blood pre ssu re de te rm in ation
S ystolic blood pre ssu re de te rm in ation
Fig. 3. Flowchart diagram depicting the oscillometric method.
Fig. 4 presents the normalized oscillation curves before and after multiplication and the
average value of diastolic, mean and systolic blood pressure as well as the value of the scaling
multiplier. The normalized amplitude matrix is then interpolated so that a single integer
pressure value corresponds to the oscillation amplitude value. If straight lines are drawn up
from the averaged diastolic and systolic IA pressure values, they cross the normalized
interpolated curve at the characteristic diastolic and systolic points (the CR values in Fig. 4).
The measured patient was a 66-year-old woman, who was 172 cm tall, weighed 75.2 kg and
had an arm circumference of 33 cm. The width of her pressure cuff was 13 cm. First, the
average values for the mean intra-arterial systolic and diastolic blood pressure are calculated.
Then, the scaling multiplier is obtained by multiplying the cuff pressure matrix by the
oscillation amplitude, to attain the 95% amplitude level of at the average mean blood
pressure. These characteristic ratios are the levels at which the multiplied matrix obtains the
averaged systolic and diastolic blood pressures values.
Molecular and Quantum Acoustics vol. 26, (2005)
245
100
Normalized oscillation (%)
95% level
80
Systolic CR=49
20
0
0
20
40
IAsys=93
Diastolic CR=32
IAmean=65
40
Scaling multiplier=0.85
IAdias=51
60
60
80
100
Cuff pressure (mmHg)
120
Fig. 4. Determination of the scaling multiplier and characteristic ratios (CR values) for
diastolic, mean and systolic blood pressure from the normalized cuff oscillation curve in the
oscillometric method.
B. BLOOD PRESSURE DETERMINATION IN THE ELECTRONIC PALPATION
METHOD
1) SYSTOLIC BLOOD PRESSURE
As shown in Fig. 5, systolic blood pressure can be easily determined at the point where
the last (or first with deflating cuff pressure mode) radial artery pulse appears in the wrist
transducer. We used an array type of transducer to facilitate the placement of the wrist
transducer, as the array element with the strongest pulsation is easy to pick out. The figure
presents the IA blood pressure signal, band-pass filtered signal and electronically palpated
signal at increasing cuff pressure levels. The filtered IA blood pressure signal has exactly the
same bandwidth as the electronically palpated signal. The palpated signal does not begin to
decrease after the point at which the cuff pressure exceeds the diastolic blood pressure. In
fact, the signal amplitude slightly increases, which is why the point where the amplitude
signal begins to decrease cannot be used to determine the diastolic blood pressure.
Consequently, diastolic pressure is determined at the point where the pulse delay begins to
increase, and systolic blood pressure at the point where the last pulse is detected. The cuff
pressure in these time points corresponds to the diastolic and systolic pressure. In this
measurement, the intra-arterial diastolic blood pressure alternated between 67 and 73 mmHg
and the systolic pressure between 152 and 162 mmHg.
246
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
Increasing cuff pressure (mmHg),
electronically palpated pulse (a.u.)
200
Intra-arterial blood pressure (mmHg),
filtered blood pressure (mmHg)
150
100
50
0
-50
-100
-150
0
5
10
15
20
Time (s)
25
30
35
Fig. 5. Determination of systolic and diastolic blood pressure in the electronic palpation
method.
2) DIASTOLIC BLOOD PRESSURE
Diastolic blood pressure determination is based on pulse transit time. When pressure in
the cuff exceeds the diastolic pressure level, pulses on the distal side of the cuff are delayed.
To accurately measure the point where this delay begins requires an accurate timing base. The
best timing reference is afforded by the radial artery of the other hand, and, if appropriate data
processing is performed, the time difference can be easily determined. Figure 6 presents a
flowchart with typical wave shapes for calculating and determining the time delay between
the pressure pulses from both hands. Firstly, the IA blood pressure and EP signals are filtered,
and the transducer element with the strongest amplitude in the EP transducer array is chosen.
After that, the signals are interpolated by a factor of ten to obtain a better timing reference.
Both signals are then derivated and the time difference is determined for each pulse on the
basis of correlation calculations. The gained matrices are then filtered using three-point
moving median and average filtering. Next, the matrices are interpolated so that every cuff
pressure integer value has a corresponding pulse delay value. Specific “triangle conversion”,
described later, is performed at the following stage to find the cut-off point for line fitting,
which is used to establish the point, at which the pulse delay begins to increase. This point
indicates the diastolic blood pressure value.
Molecular and Quantum Acoustics vol. 26, (2005)
IABP
(cath e te r)
S oftware
BP filte r
(1 14Hz )
247
EP se nsor
array
C uff
pre ssu re
Hardware
BP filte r
(1 14Hz )
Moving
ave rage LP
filte r
Extra filte ring Extra filte ring
dp/dt
dp/dt
In te rpolatio
Inte rpolatio
Inte rpolation
1
Tim e diffe re n ce
de te rm ination base d on
cross-corre lation
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
0
200
400
600
800
1000
1200
1400
1600
3 poin ts m ovin g m e dian filte ring
3 poin ts m ovin g ave rage filte rin g
In te rpolation
Triangle con ve rsion
Lin e fittin g
C rossing point de te ction
Diastolic blood pre ssu re de te rm in ation
S ystolic blood pre ssu re de te rm in ation from the first/last pu lse
Fig. 6. Flowchart diagram for the electronic palpation method.
Since the EP blood pressure signal in the radial artery has undergone analogue hardware
filtering, the IA pressure signal must be filtered similarly. Further, to eliminate noise that is
coupled to the sensor signal from the radial artery, we used additional software band-pass
filtering. Owing to the 100 Hz sampling rate used in data acquisition, the data matrices were
interpolated for better timing accuracy. According to our experience, an adequate resolution
level is achieved at a ten-fold sampling rate. After interpolation, a derivated signal is
calculated, and the time difference of these signals is worked out on the basis of correlation
calculations, i.e., best correlation corresponds to a situation where the signals overlap one
another exactly. Having clearly and accurately determined the time difference, the next step
involves locating the point where the pulse delay begins to increase. This turning point can be
accurately determined by means of “triangle conversion” after median and moving average
filtering and interpolation. In this conversion method, all time values are recalculated to form
a triangle, and line fittings are used to establish the accurate turning point. To this end, the
time values have to be inverted to obtain decreasing values, and Equation (3) can be applied
to attain new values.
248
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
Tnew (i) = Told (i) −
( Told (1) − Told (n) ) * (i − 1) − T
n −1
old ( n )
,
(3)
In this equation, Tnew(i) is the new time value for the index (i), Told(i) the old value and
(n) the index value for the last measured time value. In Fig. 7, Fig 7.1 illustrates the increase
in pulse delay during the measurement shown in Fig. 5. The pulse time difference matrix is
then inverted (Fig 7.2) and filtered using three-point moving median and average filtering
(Figs 7.3 and 7.4). In Fig 7.5, the matrix is interpolated and in Fig 7.6, “triangle conversion”
is performed to find the diastolic blood pressure. The painted dark areas corresponds each
others. Line fitting was used to locate the point at which the line crosses the 100% level. This
point correlates to diastolic blood pressure, see arrow in Fig. 7.5. This algorithm provided the
value of 67 mmHg for diastolic blood pressure, while the intra-arterial value alternated
between 67 and 72 mmHg during the measurement. The last detected pulse corresponds to the
systolic pressure and the obtained value, 157 mmHg, is within the systolic pressure range, 152
to 162 mmHg. As seen, the line fitting ranges are clearly defined by the turning point (highest
value) and, after that, the crossing point value of the fitted bolded and the 100 % level line can
be calculated. This point corresponds to the diastolic blood pressure in the low-pass filtered
cuff pressure.
Fig. 7. Determination of diastolic blood pressure from the time difference between the filtered
and derivated signals of intra-arterial and electronically palpated blood pressure pulses.
Molecular and Quantum Acoustics vol. 26, (2005)
249
3. RESULTS
A. ACCURACY OF THE OSCILLOMETRIC METHOD IN BLOOD PRESSURE
DETERMINATION
The oscillation method described earlier was used to determine characteristic ratios
separately for the healthy volunteer and the cardiac patient group. For the first group,
measurement mode (i.e., inflating and deflating cuff pressure) and position (either supine or
sitting) were also considered. Table 3 tabulates the average values for these factors along with
standard deviations and scaling multipliers for both groups. As the table shows, the scaling
multiplier for the healthy group is about one in every group. The table shows only a slight
deviation between measurement mode and position (sitting or supine). The characteristic
ratios were found to range from 40% to 48% for diastolic blood pressure and from 49% to
59% for systolic blood pressure in the different modes. The values for diastolic characteristic
ratios differ considerably from the mean values reported by Geddes (82%), Drzewiecki (72%)
and Ursino (70%). Geddes conducted his measurements on humans, while Drzewiecki and
Ursino also used mathematical models and a simple blood pressure signal mode. One
explanation to account for the observed discrepancy between their results and the ones
reported here is the different oscillation filtering bandwidth used in our experiments. The
characteristic ratios for systolic blood pressure obtained in the present study lay within the
same range as those reported in the earlier experiments (55%, 59%, 52%, respectively).
Another interesting finding is that the standard deviation of the characteristic ratio for
diastolic blood pressure was only about 50% of that calculated for systolic pressure.
With the cardiac patient group, the scaling multipliers were less than one, 0.86 or 0.87,
depending on the filtering mode. The average values of the characteristic ratios are somewhat
lower for systolic blood pressure and the standard deviations slightly higher than the
corresponding figures for the healthy group. The oscillation amplitude curve was gained by
subtracting the two-second moving average filtered cuff oscillation from the unfiltered
oscillation curve (SW filtering), which resulted in low degree of distortion. As analogically
filtered signals (HW filtering) gave almost identical characteristic ratios, filtering did not have
a great effect on accuracy.
250
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
Tab. 3 - Characteristic ratios for diastolic and systolic blood pressure in the healthy and the
cardiac patient group.
Mode
Healthy
Inflating
Deflating
Patients
Inflating,
Characteristic ratios, %
Supine
Sitting
Supine
Sitting
Diastolic
Systolic
39.6 ± 7.4
45.7 ± 9.4
44.3 ± 6.6
48.1 ± 8.7
48.5 ± 11.6
55.2 ± 16.7
51.2 ± 13.2
58.5 ± 15.3
Supine 45.8 ± 15.6
HW filtering
Inflating,
Supine 43.9 ± 10.4
SW filtering
Scaling
multiplier
0.96 ± 0.05
1.01 ± 0.08
1.02 ± 0.05
1.08 ± 0.06
40.6 ± 12.3 0.87 ± 0.07
47.1 ± 13.9 0.86 ± 0.07
These average characteristic ratio values were used to determine both the systolic and
diastolic pressure as well as the pressure errors for both groups. The reference values were
averaged from the IA pressure waveforms during cuff inflation or deflation, and the mean
artery pressure was determined at the point where the cuff oscillation reached 95% of its
maximum amplitude curve. The results are presented in Table 4. The pressure error in the
healthy group was found to be fairly low, except in the case of sitting test subjects measured
during deflation of the cuff, which is the standard procedure used in clinics. This may be due
to hydraulic head, i.e., difference in blood level between the reference transducer (catheter)
and the cuff. For the patient group, however, the average errors were high, producing
overestimates in excess of 10 mmHg, which is outside the standard range. It must be pointed
out that the width of the cuff used in these measurements was 40% or more of the subjects’
arm circumference, thus fulfilling the requirements of the standards. In both groups, the
standard deviation of errors for systolic blood pressure was too high to fit the standards,
regardless of cuff pressure mode and posture. On the other hand, in measurements of diastolic
and mean artery pressure, these deviations were less than 8 mmHg, which fulfils the
requirements.
Molecular and Quantum Acoustics vol. 26, (2005)
251
Tab. 4 - Accuracy of the diastolic, mean and systolic blood pressure measurements in the
healthy and the cardiac patient group using the OSC method. The reference value is the
average value of the IA diastolic, mean and systolic blood pressure.
Mode
Healthy
Inflating
Deflating
Patients
Inflating,
HW filtering
Inflating,
SW filtering
Posture
Mean errors
Diastolic Mean
Systolic
(mmHg) (mmHg)
(mmHg)
2.8 ± 4.5 4.1 ± 5.0
−0.3 ± 5.40.1 ± 6.9
−1.1 ± 5.1−1.4 ± 3.7
−4.6 ± 6.3−6.5 ± 4.5
4.0 ± 8.4
−1.9 ± 10.1
−3.3 ± 8.5
−9.0 ± 11.0
Supine 12.7 ± 6.113.4 ± 6.8
15.0 ± 11.6
Supine 10.0 ± 5.312.3 ± 7.0
10.2 ± 12.0
Supine
Sitting
Supine
Sitting
B. ACCURACY OF THE ELECTRONIC PALPATION METHOD IN BLOOD PRESSURE
DETERMINATION
Systolic pressure was determined visually from the last palpated pulse, while diastolic
blood pressure readings were obtained by the time delay method described above. Reference
values were provided by the average IA systolic and diastolic pressure. The corresponding
accuracies are presented in Table 5. For the healthy group, the measured accuracies are about
the same or lower than in the OSC method during both inflation and deflation of the cuff. On
the other hand, the deflating mode yielded better results for diastolic pressure and inflating
mode for systolic pressure. For the cardiac patient group, the mean error is -0.6 ± 5.1 mmHg
for diastolic and 0.7 ± 6.3 mmHg for systolic pressure, which are considerably better than the
accuracies obtained with the OSC method and fulfil both standards.
252
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
Tab. 5. Accuracy of diastolic and systolic blood pressure measurements in the healthy and the
cardiac patient group using the EP method. The reference value is the average value of the IA
diastolic, mean and systolic blood pressure.
Mode
Healthy
Inflating Supine
Sitting
Deflating Supine
Sitting
Patients
Inflating Supine
Mean errors
Diastolic Systolic
(mmHg)
(mmHg)
−5.5 ± 5.2
−11.2 ± 6.1
2.0 ± 6.6
0.1 ± 7.3
−1.7 ± 7.9
−3.2 ± 6.7
−6.3 ± 9.2
−7.1 ± 8.3
−0.6 ± 5.1 0.7 ± 6.3
Other possible determinants for systolic, mean and diastolic blood pressure errors were
examined by means of correlation calculations. Correlations between pressure errors and IA
pressures are fairly low, the only exception being the correlation between the diastolic
pressure errors of the IA and the EP method, which is 0.54. We also noted that the pressure
errors were unaffected by the transducer array’s amplitude level.
4. DISCUSSION
The mean errors obtained with the OSC method in the cardiac patient group were too
large to meet the AAMI and BHS standards, which call for an inaccuracy level of less than 5
mmHg and a standard deviation of less than 8 mmHg. As for the EP method, the errors were
considerably lower, fulfilling the requirements of the standards. One possible explanation for
the large errors in the OSC method is that the cuff actually serves as a nonlinear sensor. Our
analysis employed a scaling multiplier to get the 95% level of the oscillation curve to cross at
the mean intra-arterial pressure point. Without a scaling multiplier, the maximum oscillation
in Fig. 4 is about 78 mmHg, which overestimates the mean blood pressure by 13 mmHg. The
corresponding figures for the EP method in the same measurement are 56/94 mmHg for the
diastolic and systolic pressure, while the intra-arterial diastolic and systolic pressures alternate
between 47 and 54 mmHg and 89 and 99 mmHg, respectively. This shows that point where
the change of delay appears gives a reasonably good estimation of the diastolic blood
pressure. In the OSC method, however, the maximum oscillation amplitude is attained at a
pressure higher than the mean intra-arterial pressure, producing false readings.
In the EP method, when cuff pressure exceeds the venous pressure level, the volume of
blood in the hand starts to increase with each heart beat. This induces vascular congestion and
a pressure increase in the veins and surrounding tissues in the distal side of the cuff. The
Molecular and Quantum Acoustics vol. 26, (2005)
253
velocity of the pressure pulse waves and therefore also the delay change depends
exponentially on vascular compliance and the pressure difference between the inner and outer
arterial wall. The change of this pressure, also known as transmural pressure, produces a
decrease in the pulse wave velocity and a slight increase in the pulse delay, before the
diastolic blood pressure level is achieved. This phenomenon is illustrated in Fig. 8. Intraarterial (IA) blood pressure is measured invasively with the catheter tip and radial artery
pulsation noninvasively with the electronic palpation transducer. The recorded venous
pressure increases slightly until the cuff pressure drops under it. The same phenomenon
occurs also with inflating cuff pressure. Owing to venous congestion, the pulse is still slightly
delayed at cuff pressures between the diastolic and intra-venous (IV) pressure. The vertical
line in centre represents the average diastolic blood pressure value during the measurement
and the other lines its minimum and maximum values. According to our experiments (Sorvoja
et al 2003), the deflating mode is preferable, due to a smaller increase in vascular pressure.
Because this paper used only inflating cuff pressure with the cardiac patient group, additional
Cuff pressure,
IA & IV pressure (mmHg)
comparison and assessment.
200
0
Time delay (ms)
-200
5
10
15
50
20
25
Time (s)
30
Electronic palpation signal (a.u.)
research focusing on the effects of decreasing pressure is required for a more conclusive
35
40
120
140
0
-50
0
20
40
60
80
100
Cuff pressure (mmHg)
Fig 8. Pulsations of the left and right radial arteries and cuff pressure (upper Fig.) as a
function of time and pulse delay between the pulses (lower Fig.) as a function of cuff
pressure. Vertical lines in the lower Fig. are minimum, average and maximum values of IA
diastolic blood pressure.
254
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
In the EP method, the ECG signal, being easily detectable in normal subjects, can be used
as a timing reference. However, since electrical cardiac activation (ECG) does not constitute
mechanical activation (Hong 2000), it is advisable to use pressure pulses instead. The R-spike
is followed by a pre-ejection period before the aortic valve opens. This time period depends
on the aortic diastolic pressure and the heart constriction rate, and the resulting deviation in
the pressure pulse delay renders the determination of blood pressure inaccurate (Meigas et al
2001). The optimal reference point is the radial artery of the other hand, which usually
produces almost an identical pulse waveform, thereby greatly facilitating the accurate
detection of pulse delays. This method does not provide any information on pulse wave
velocity, which could be used for the continuous, pulse-to-pulse monitoring of blood pressure
(Chen et al 2000, Meigas et al 2001 and 2003). Nevertheless, pulse wave velocity can be
clearly specified in measurements on one hand using, for example, the brachial and radial
artery.
The shape of pressure pulses, especially optically measured plethysmographic pulses, is
quite smooth, and does not provide a very distinct timing point. A better timing reference may
be obtained with optical interferometers, such as laser-Doppler systems based on the selfmixing effect (Hast et al 2001, Hast 2003 and Meigas et al. 2001 and 2003). These
interferometers measure skin vibrations over an artery with a very high degree of accuracy,
restricted theoretically only by the wavelength of the laser divided by two (~300 - 700 nm).
Their main advantage is the absence of a loading effect, as the sensor is not in contact with the
skin. Also, unlike tonometer type pressure transducers (Drzewiecki et al 1983 and 1996, Kelly
et al 1989, Kemmotsu et al 1991, Bansal et al 1994, Drzewiecki and Pilla 1998, Fetics et al
1999), laser-Doppler interferometers do not suffer from sensor movements caused by arterial
pulsations. Further, their timing accuracy is very good, because the signal has a much wider
bandwidth than a pressure signal.
In principle, the time delay method can be used with any transducer that measures
pulsations either on the radial artery or a finger. One possibility is to measure noninvasively
radial artery pressure pulses in the both hands using either two identical pressure array
transducers or interferometers with the same bandwidth. In addition, by measuring the starting
time of pressure pulses (opening of the aortic valve) by using, for example, contact
microphone on a chest, we will be able to determine the pulse waveform and pulse wave
velocity (PWV), which enables noninvasive, pulse-by-pulse monitoring of blood pressure.
This needs a daily calibration measurement by using a cuff around an arm. Also pressure
pulses measured in the brachial artery under the cuff can be used as a timing base. Finally,
detecting the waveform, amplitude and time of pulsations in the radial artery can be utilized in
Molecular and Quantum Acoustics vol. 26, (2005)
255
many ways: for example, with other noninvasively measured physiological signals, they can
be used to determine arterial compliance or stiffness (O’Rourke et al 1992, Asmar et al 1995,
Safar et al 1998, Blacher et al 1999, O’Rourke and Mancia 1999 and Asmar 1999) and central
aortic pressure (Kelly et al 1989, Karamonoglu et al 1993, Chen et al 1997 and Fetics et al
1999).
5. CONCLUSIONS
Using intra-arterial blood pressure as a reference, this study investigated two measuring
methods, the oscillometric (OSC) and the electronic palpation (EP) method. The test subjects
comprised a group of healthy volunteers and a group of cardiac patients in intensive care.
These patients had undergone either a bypass or a valve operation, or both. The healthy group
was measured twice using both inflating and deflating cuff pressure in a sitting and supine
position with a one-minute follow-up period between the measurements. The cardiac patient
group, on the other hand, was measured only in the supine position with increasing cuff
pressure. All measurements were analyzed with the aim of ensuring the best possible accuracy
level for both groups. Thus, in the OSC method, case-specific characteristic ratios were used
to reach an ideal degree of accuracy. In the EP method, the determination of systolic blood
pressure was based on the last palpated pulse, while the determination of diastolic pressure
relied on changes in the time delay of the detected signals produced by an occlusion cuff. For
better timing accuracy, interpolation was used to increase the sampling rate by a factor of ten.
In the oscillometric method, the acquired characteristic ratios for the determination of
diastolic blood pressure differed considerably from those reported in the literature, whereas
the ratios for systolic blood pressure were within the reported ranges. It was also established
that the cuff pressure mode affects accuracy: in the deflating mode, the characteristic ratios
were slightly higher than in the inflating mode. Also posture had an effect: both characteristic
ratios increased by about 4 - 8 percent when the test subjects were sitting. Age, on the other
hand, seemed to have only a minute effect on these ratios, provided that the same cuff
pressure mode and position were used in all measurements.
In the healthy group, the EP method was found to provide a slightly better degree of
accuracy with deflating cuff pressure. With a mean error of about zero and a standard
deviation of about 5 – 6 mmHg, also the inflating mode used in the cardiac patient group gave
excellent results. Nevertheless, to avoid vascular swelling, the deflating pressure is the more
recommendable of the two. To sum up, a comparison between the OSC and the EP method
indicates that the EP method offers a feasible alternative to the OSC method, particularly
because it also produces information on the delay and waveform of pressure pulses. As a
256
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
consequence, it can be applied to continuous blood pressure monitoring and the noninvasive
determination of such physiological indices as arterial compliance and central aortic pressure.
ACKNOWLEDGMENTS
The first author would like to thank Polar Electro Oy for funding and the Tauno Tönning
Foundation for supporting this work through two grants and Professor Alexander Priezzhev
from the Moscow State University for scientific advice.
REFERENCES
1. Alexander, H., M. Cohen, L. Steinfeld. Criteria in the choice of an occluding cuff for the
indirect measurement of the blood pressure. Med. Biol. Eng. Comput. 15:2, 1977.
2. Asmar, R., A. Benetos, J. Topouchian, P Laurent., B pannier, A-M. Brisac, R. Target, B. I.
Levy. Assessment of arterial distensibility by automatic pulse wave velocity
measurement. Hypertension. 26(3): 485-490. 1995.
3. Asmar R. Arterial Stiffness and Pulse Wave Velocity, Clinical Applications. Éditions
scientifiques et médicales Elsevier SAS. 23, rue Linois, 75724 Paris cedex 15, 1999. 167
p.
4. Association for the Advancement of Medical Instrumentation. American national
standard. Electronic or automated sphygmomanometers. ANSI/AAMI SP 10-1987.
Arlington, VA: AAMI, 1987:25.
5. Association for the Advancement of Medical Instrumentation. American national
standard. Electronic or automated sphygmomanometers. ANSI/AAMI SP 10-1992.
Arlington, VA: AAMI, 1993:1-40.
6. Bansal, V., G. Drzewiecki, R. Butterfield. Design of a flexible diaphragm tonometer,
Proc. 13th S. Bioeng. Conf. Washington, DC, pp. 148-151, 1994.
7. Blacher J., R. Asmar, S. Djane, G. M. London, M. E. Safar. Aortic pulse wave velocity as
a marker of cardiovascular risk in hypertensive patients. Hypertension. 33 (5): 1111-1117.
1999.
8. Chen, C-H., E. Nevo, B. Fetics, P. H. Pak, F. C. P. Yin, L. Maughan, D. A. Kass.
Estimation of central aortic pressure by mathematical transformation of radial tonometry
pressure; validation of generalized transfer function. Circulation. 95:1827-1836, 1997.
9. Chen, W., T. Kobayashi, S. Ichikawa, Y. Takeuchi, T. Togawa. Continuous estimation of
systolic blood pressure using the pulse arrival time and intermittent calibration. Med. Biol.
Eng. Comput. Vol. 38, no. 5, pp. 569-574, 2000.
10. Cristalli, C., M. Ursino, F. Palagi, R. Bedini. FEM simulation and experimental evaluation
of the 'squeezing' phenomenon in Riva-Rocci blood pressure measurement. Comput.
Cardiology. Proc., pp. 655–658, 1993.
11. Cristalli, C., Neuman MR, Ursino M. Studies on soft tissue pressure distribution in the
arm during non-invasive blood pressure measurement. Engineering in Medicine and
Biology Society, Engineering Advances: New Opportunities for Biomedical Engineers,
Proceedings of the 16th Annual International Conference of the IEEE, Vol. 1, pp. 41-42,
1994.
12. Cristalli, C., P. Mancini, M. Ursino. An experimental and mathematical study of
noninvasive blood pressure measurement. Engineering in Medicine and Biology Society,
Molecular and Quantum Acoustics vol. 26, (2005)
257
1992. Vol.14. Proc. of the Annual International Conference of the IEEE, Volume: 1, 29
Oct-1 Nov 1992, pp. 63 –64, 1992.
13. Drzewiecki, G., B. Solanki, J_J. Wang, J. K_L: Li. Noninvasive determination of arterial
pressure and volume using tonometry. ELECTRO '96. Professional Program. Proceedings.
pp. 61-63, 30 April - 2 May, 1996.
14. Drzewiecki, G., J. J. Pilla. Noninvasive measurement of the human brachial artery
pressure-area relation in collapse and in hypertension. Ann. Biomed. Eng. Vol. 26, pp.
967-974, 1998.
15. Drzewiecki, G., J. Melbin, A. Noordengraaf. Arterial tonometry: review and analysis. J.
Biomech. 16:141-152, 1983.
16. Drzewiecki, G., Noninvasive assessment of arterial blood pressure and mechanics. In: The
Biomedical Engineering Handbook, edited by J.D. Bronzino, Trinity College, Hartford,
Connecticut, U.S.A., Chapt. 73, p. 1198, 1995.
17. Drzewiecki, G., R. Hood, A. Apple. Theory of the oscillometric maximum and the
systolic and diastolic detection ratios. Ann. Biomed. Eng. Vol. 22, pp. 88-96, 1994.
18. Drzewiecki, G., V. Bansal, E. Karam, R. Hood, A. Apple. Mechanics of the occlusive arm
cuff and its application as a volume sensor. IEEE Trans. Biomed. Eng. Vol. 40, pp. 704708, July 1993.
19. Fetics, B., E. Nevo, C-H. Chen, D. A. Kass. Parametric Model derivation of transfer
function for noninvasive estimation of aortic pressure by radial tonometry. IEEE Trans.
Biomed. Eng. Vol. 46, no. 6, 1999.
20. Ficher, M, K. Kirjavainen, P. Vainikainen, E. Nyfors. Sensor for the measurement of
pressure. Proc. of 20th European microwave conference, Budapest, Hungary, pp. 985-989,
Sept. 1990.
21. Geddes, L. A. Handbook of Blood Pressure Measurement. Humana Press. 1991.
22. Geddes, L. A., M. Voelz, C. Combs. Characterization of the oscillometric method for
measuring indirect blood pressure. Ann. Biomed. Eng. Vol. 10, pp. 271-280, 1983.
23. Geddes, L. A., M. Voelz, S. James, D. Reiner. Pulse wave velocity as a method of
obtaining systolic and diastolic blood pressure. Med. Biol. Eng. & Comput. Vol. 19, pp.
671-672, 1981.
24. Geddes, L. A., S. J. Whistler. The error in indirect blood pressure measurement with
incorrect size of cuff. Am. Heart. J. 96:4, 1978.
25. Hast, J. T. Self-Mixing Interferometry and Its Applications in Noninvasive Pulse
Detection. Oulu, Finland: Dissertation thesis, University of Oulu, Department of Electrical
Engineering, 2003, 72 p.
26. Hast, J. T., R. A. Myllylä, H. S. S. Sorvoja, J. Miettinen. Self-mixing interferometry in
noninvasive pulse wave velocity measurement. Molecular and Quantum Acoustic, Vol. 22
(2001), pp. 95-106, 2001.
27. Hong, H. Optical Interferometric Measurement of Skin Vibration for the Diagnosis of
Cardiovascular Diseases. Michican: UMI Dissertation Services, A Bell & Howell
Company, pp. 111-113, 2000.
28. Karamanoglu M., M. F. O’Rourke, A. P. Avolio, R. P. Kelly. An analysis of the
relationship between central aortic and peripheral upper limb pressure waves in man. Eur.
Heart J. 14:160-167, 1993.
29. Kelly R., C. Hayward, J. Ganis, J. Daley, A. Avolio, M. O’Rourke. Noninvasive
258
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
registration of the arterial pressure pulse waveform using high-fidelity applanation
tonometry. J. Vasc. Med. Biol. 1:142-149, 1989.
30. Kemmotsu O., M. Ueda, H. Otsuka, T. Yamamura, D. C. Winter, J. S. Eckerle. Arterial
tonometry for non-invasive, continuous blood pressure monitoring during anaesthesia.
Anaesthesiology. 75:333-340, 1991.
31. Kerola, J., V. Kontra, R. Sepponen. Noninvasive blood pressure data acquisition
employing pulse transit time detection. 18th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society, Amsterdam 1996, 5.3.1: Vessel Dynamics,
pp. 1308-1309, Jan. 1997.
32. Kirjavainen, K. Electomechanical film and procedure for manufacturing same. US Patent
4,654,546, 9 p., March 1987.
33. Maguire, M., T. Ward, C. Markham, D. O’Shea, L. Kevin. A comparative study in the use
of brachial photoplethysmography and the QRS complex as timing reference in
determination of pulse transit time. Engineering in Medicine and Biology Society, 2001.
Proceedings of the 23rd Annual International Conference of the IEEE, Vol. 1, 2001, pp.
215-218, 2001.
34. Meigas, K, R. Kattai, J Lass. Continuous blood pressure monitoring using pulse wave
delay. Engineering in Medicine and Biology Society, Proc. of the 23 rd Annual
International Conference of the IEEE, Vol. 4, pp. 3171–3174, 2001.
35. Meigas, K., H. Hinrikus, R. Kattai, J. Lass. Self-mixing in a diode laser as a method for
cardiovascular diagnostics. J. Biomed. Optics. 8(1), pp. 152-160, 2003.
36. Nissilä, S. M., M. Sorvisto, H. S. S. Sorvoja, E. I. Vieri-Gashi, R. A. Myllylä.
Noninvasive blood pressure measurement based on the electronic palpation method. Proc.
of the 20th Annual International Conference of the IEEE Engineering in Medicine and
Biology Society, Vol. 20, No 4, pp. 1723-1726, 1998.
37. O’Brien, E., B. Waeber, G. Parati, J Staessen, M. G. Myers. Clinical Review: Blood
pressure measuring devices: recommendations of the European Society of Hypertension.
BMJ., Vol. 322, pp. 531-536, 2001.
38. O’Brien, E., J. Petrie, W. Littler, et al. Short report: an outline of the revised British
Hypertension Society protocol for the evaluation blood pressure measuring devices. J.
Hypertens. Vol. 11, pp. 677-679, 1993.
39. O’Brien, E., J. Petrie, W. Littler, et al. The British Hypertension Society protocol for the
evaluation of automated and semi-automated blood pressure measuring device with
special reference to ambulatory systems. J. Hypertens. Vol. 8, pp. 607-619, 1990.
40. O’Rourke, M., G. Mancia. Arterial Stiffness. J. Hypertens. Vol. 17, no. 1, pp.1-4, 1999.
41. O’Rourke, M., R. Kelly, A. Avolio. The Arterial Pulse. 200 Chester Field Parkway,
Malvern, Pennsylvania 19355-9725, U.S.A., Lea & Febiger, 1992, 239 p.
42. Ruha, A. Detection of Heart Beat in Ambulatory Heart Rate Measurements, Methods and
Integrated Circuits. Oulu, Finland: Dissertation thesis, University of Oulu, Department of
Electrical Engineering, 1998, pp. 36-42.
43. Ruha, A., J. Miettinen, H. S. S. Sorvoja, S. M. Nissilä. Heart rate measurement based on
detection of the radial artery pressure pulse. Proceedings of BIOSIGNAL’96, Brno, Czech
Republic, June 25-27 1996, 1:198-200, 1996.
44. Safar M. E., G. M. London, R. Asmar, E. D. Frohlich. Recent advances on large arteries in
hypertension. Hypertension, 32: 156-161, 1998.
Molecular and Quantum Acoustics vol. 26, (2005)
259
45. Šantić, A., I. Lacrović. Simultaneous application of multiple oscillometric methods for
blood pressure measurement in finger. Proceedings of The First Joint BMES/EMBS
Conference Serving Humanity, Advancing Technology, Atlanta, GA, USA, p. 231, Oct.
1999.
46. Šantić, A., M. Šaban. Two methods for determination of diastolic pressures in fingers.
1995 IEEE-EMBC and CMBEC, Theme 1, Cardiovascular System, pp. 149-150, July
1997.
47. Sorvoja H., R. Myllylä: Accuracy of the electronic palpation blood pressure measurement
method versus the intra-arterial method. Technology and Health Care. International
Journal of Health Care, IOS Press, Vol. 12, No. 2, pp. 145-146, 2004.
48. Sorvoja H. S. S., R. A. Myllylä. Accuracy of the electronic palpation method. The 7th
Conference of the European Society for Eng. and Medicine (ESEM), p. 196, 2003.
49. Sorvoja H., J. Hast, R. Myllylä, P. Kärjä-Koskenkari, S. Nissilä, M. Sorvisto. Blood
pressure measurement method using pulse-transit-time. Mol. Quant. Acoust. Vol. 24, pp.
169-181, 2003.
50. Sorvoja, H. S. S., R. A. Myllylä, S. M. Nissilä, P. Kärjä-Koskenkari, J. K. Koskenkari, M.
K. Lilja, Y. A. Kesäniemi. A method to determine diastolic blood pressure based on
pressure pulse propagation in the electronic palpation method. Engineering in Medicine
and Biology Society, Proc. of the 23rd Annual International Conference of the IEEE, Vol.
1, 2001, pp. 3255-3258, 2001.
51. Sorvoja, H. S. S., R. A. Myllylä, S. M. Nissilä, P. Kärjä-Koskenkari, J. K. Koskenkari, M.
K. Lilja, Y. A. Kesäniemi. Systolic blood pressure accuracy enhancement in the electronic
palpation method using pulse waveform. Engineering in Medicine and Biology Society,
Proc. of the 23rd Annual International Conference of the IEEE, Vol. 1, 2001, pp. 222 –225,
2001.
52. Sorvoja. H. S. S., V-M. Kokko, R. A. Myllylä, J. Miettinen. Use of EMFi as a blood
pressure pulse transducer. IEEE Trans. Intr. & Meas. Vol. 54. no. 5, Oct. 2005.
53. Ursino, M., C. Cristalli. A mathematical study of some biomechanical factors affecting the
oscillometric blood pressure measurement. IEEE Trans. Biomed. Eng. Vol. 43, no. 8, pp.
761-778, Aug. 1996.
54. Ursino, M., C. Cristalli. Mathematical analysis of the oscillometric technique for indirect
blood pressure evaluation. Engineering in Medicine and Biology Society, Engineering
Advances: New Opportunities for Biomedical Engineers, Proceedings of the 16th Annual
International Conference of the IEEE, Vol.2, pp. 1286-1287, 1994.
55. van der Hejden-Spek, J. J., J. A. Staessen, R. H. Fagard, A. P. Hoeks, H. A. S. Boudier, L.
M van Bortel. Effect of age on brachial artery wall properties differs from the aorta and is
gender dependent. Hypertension. 35 (2): 637-642, 2000.
56. van Popele, N. M., D. E. Grobbee, M. L. Bots, R. Asmar, J. Topouchian, R. S. Reneman,
A. P. G. Hoeks, D. A. M. van der Kuip, A. Hofman, .J. C. M. Witteman. Association
between arterial stiffness and atherosclerosis: the Rotterdam study. Stroke. 32: 454-460.
2001.
57. van Popele, N. M., W. J. W. Bos, N. A. M. de Beer, D. A. M van der Guip, A. Hofman, D.
E. Groppee, J C. M. Wittemann. Arterial stiffness as underlying mechanism of
disagreement between an oscillometric blood pressure monitor and a sphygmomanometer.
Hypertension. 36 (4): 484-488, 2000.
58. Vieri-Gashi, E. I., H. S. S. Sorvoja, R. A. Myllylä, S. M. Nissilä, M. Sorvisto, P. KärjäKoskenkari. The effect of the venous pressure to the blood pressure signals measured by
260
Sorvoja H., Myllylä R., Kärjä-Koskenkari P., Koskenkari J., Lilja M., Kesäniemi Y.A.
the palpation method. Engineering in Medicine and Biology Society, Proc. of the 23rd
Annual International Conference of the IEEE, Vol. 1, 2001, pp. 3259-3261, 2001.
59. Vieri-Gashi, E. I., P. Kärjä-Koskenkari, S. M. Nissilä, M. Sorvisto, H. S. S. Sorvoja, R. A.
Myllylä. Blood pressure variation measured by the electronic palpation method and
compared to intra-arterial variation. Proceedings of Işik 2000 Workshop on Biomedical
Information Engineering, 25-27. June, Istanbul, Turkey, pp. 101-103, 2000.