ANALYSIS OF HEART RATE AND BLOOD PRESSURE

VARIABILITY IN PREGNANCY

New Method for the Prediction of Preeclampsia

H. Malberg

Karlsruhe Research Center, Institute for Applied Computer Science, Herrmann-von-Helmholtz-Platz 1

76344 Eggenstein-Leopoldshafen, Germany

R. Bauernschmitt

Clinic for Cardiovascular Surgery, German Heart Center Munich, Germany

T. Walther

Department of Cardiology, Charité, Campus Benjamin Franklin (CBF), Berlin, Germany

A. Voss

Department of Medical Engineering, University of Applied Sciences, Jena, Germany

Renaldo Faber, Holger Stepan

Department of Obstetrics and Gynecology, University of Leipzig, Germany

N. Wessel

Department of Physics, University of Potsdam, Germany

Keywords: Heart Rate Variability, Blood Pressure Variability, baroreceptor reflex; screening, risk stratification.

Abstract: Pre-eclampsia is a serious disorder with high morbidity and mortality occurring during pregnancy; 3%–5%

of all pregnant women are affected. Although most pre-eclamptic patients show pathological uterine perfu-

sion in the second trimester, this parameter has a positive predictive accuracy of only 30%, which makes it

unsuitable for early, reliable prediction. The study is based on the hypothesis that alterations in cardiovascu-

lar regulatory behavior can be used to predict PE. Ninety-six pregnant women in whom Doppler investiga-

tion detected perfusion disorders of the uterine arteries were included in the study. Twentyfour of these

pregnant women developed PE after the 30th week of gestation. During pregnancy, additional several non-

invasive continuous blood pressure recordings were made over 30 min under resting conditions by means of

a finger cuff. In the period between the 18th and 26th weeks of pregnancy, three special variability and

baroreflex parameters were able to predict PE several weeks before clinical manifestation. Discriminant

function analysis of these parameters was able to predict PE with a sensitivity and specificity of 87.5% and

a positive predictive value of 70%. The combined clinical assessment of uterine perfusion and cardiovascu-

lar variability demonstrates the best current prediction several weeks before clinical manifestation of PE.

1 INTRODUCTION

Pre-eclampsia (PE) is a serious pregnancy-specific

disorder. It is characterized by sudden hypertension

>140/90 mm Hg and a proteinuria (>300 mg in 24

hours). The manifestation of PE is the main cause of

maternal and neonatal morbidity and mortality; it

occurs in 3-5 % of all pregnancies.

Although the etiology and pathogenetic factors

of the disease are largely unknown, early risk as-

sessment by Doppler sonography has become an

established procedure. However, the positive predic-

tive accuracy (PPA) of Doppler sonography is lim-

ited to 30 %, as pregnant women with disturbed

uterine perfusion may develop a PE, a pregnancy-

induced hypertension (PIH), or a neonatal intra-

uterine growth retardation (IUGR) (Chien, 2000).

Malberg H., Bauernschmitt R., Walther T., Voss A., Faber R., Stepan H. and Wessel N. (2008).

ANALYSIS OF HEART RATE AND BLOOD PRESSURE VARIABILITY IN PREGNANCY - New Method for the Prediction of Preeclampsia.

In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing, pages 47-55

DOI: 10.5220/0001066400470055

 SciTePress

Earlier studies were unable to find either inde-

pendent markers in the maternal plasma or physio-

logical parameters easy to measure and, in this way,

improve the screening efficacy of Doppler sonogra-

phy (Benedetto, 1998).

Analyses of heart rate variability (HRV), systolic

(SBPV) and diastolic blood pressure variability

(DBPV) and baroreflex (BR) sensitivity (BRS) were

able to demonstrate their high diagnostic and prog-

nostic powers in various studies characterizing

autonomous cardiovascular regulation in various

diseases (La Rovere, 2001). Various studies demon-

strated the suitability of these methods in hyperten-

sive disorders of pregnancy, such as chronic hyper-

tension (Walther, 2005), gestational hypertension

(Hermida, 1998), and in PE (Faber, 2004). However,

these diseases were clinically manifest already at the

time of examination.

In contrast to those other studies, this study em-

ploys the approach of looking for characteristic al-

terations in cardiovascular regulation before the

sudden rise of blood pressure. The study is based on

the hypothesis that alterations in cardiovascular

regulatory behavior can be used to predict PE. Con-

ventional clinical prediction, i.e. Doppler sonogra-

phy, has to be taken into account. Earlier findings

have shown that the sole use of variability analysis

in the 18th – 22nd weeks of gestation (WOG) was

able to attain a PPA of 50 % (Walther, 2006). In

addition, a combined study of variability and uterine

perfusion achieved a PPA of 71.6 %, which may be

considered the best finding for a non-invasive risk

marker of PE at this point in time. In the study out-

lined below, findings are to be validated in an ex-

tended group of patients over a longer period of ex-

amination between the 18th and the 26th weeks of

gestation.

2 PATIENTS

96 patients with abnormal uterine perfusion (AUP)

were included in the study. All pregnant women

underwent Doppler sonography in the 2nd trimester

of pregnancy (median 22nd week of gestation,

WOG, range 18 – 26 weeks) at the Department of

Obstetrics and Gynecology of the University of

Leipzig. 24 of these pregnant women developed PE

after the 30th week of gestation. Approval by the

local ethics committee and the informed consent of

all subjects were obtained. All pregnancies were

singleton. At the time of examination, the women

were healthy, normotensive, without clinical signs of

cervical incompetence, and on no medication.

Clinically, the development of pregnancy was

subdivided in accordance with PE, pregnancy-

induced hypertension (PIH), intrauterine growth

retardation (IUGR), or pregnancy without any fur-

ther complications. PE was classified in line with the

guidelines of the International Society for the Study

of Hypertension in Pregnancy. PIH was described by

the rise of several blood pressure levels to more than

140 mm Hg in the systole and more than 90 mm Hg

in the diastole within four hours. Significant pro-

teinuria is characterized by an excretion of more

than 300 mg of total protein in 24 hours. Where

these data were not available, proteinuria was de-

tected by dipstick on two consecutive occasions

within four hours. Intrauterine growth retardation

was defined by the birth weight being below the

10th percentile of a reference group.

3 METHODS

Doppler examination of the uterine arteries was car-

ried out with a LOGIQ 9 ultrasound machine (GE,

Solingen, Germany) with a 5 MHz convex trans-

ducer by the same sonographer. Uterine perfusion

was classified as pathological when there was bilat-

eral notching or a mean pulsatility index (PI) of both

arteries above 1.45. Immediately after the Doppler

examination, continuous blood pressure monitoring

was conducted non-invasively via finger cuff (100

Hz, Portapres device mod. 2, BMI-TNO, Amster-

dam, The Netherlands). The measurements were

performed under standardized resting conditions

between 8 a.m. and 12 noon. The continuous blood

pressure curves were used to extract the time series

of beat-to-beat intervals, systolic and diastolic blood

pressures.

3.1 Preprocessing

The main objective of the analysis of heart rate and

blood pressure is to investigate the cardiovascular

system during normal sinus rhythm. Therefore, it is

necessary to exclude not only artifacts (e.g. double

recognition, i.e. R-peak and T-wave recognized as

two beats) but also beats not coming from the sinus

node of the heart, so called ventricular premature

complexes (VPC). VPCs are not directly controlled

by the autonomous nervous system. Practically, this

exclusion means filtering of the time series. The

original time series are denoted RR-series (derived

from the RR-intervals) and the filtered time series,

NN-series (normal-to-normal beat interval, NNI).

VPCs in the tachogram are usually characterized by

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

regular ventricular premature beat and supraven-

tricular premature beat complexes). The 20%-filter

(Kleiger, 1987) considers these facts; if the current

value of the tachogram differs from its predecessor

by more than 20%, the current value and its succes-

sor are marked not normal. VPCs with less than 20%

difference are not removed from the series and may

falsify almost all HRV or BPV parameters. The RR-

intervals recognized as not normal are treated in

different ways: either they are simply removed from

the series or interpolated linearly or spline interpo-

lated (Lippmann, 1994). Interpolating linearly may

lead to false decreased variability’s, interpolating

with splines often fails in time series with many

VPCs. In several clinical studies, an adaptive filter-

ing algorithm introduced in (Wessel, 2000) has been

proven to exclude premature beats and artifacts. The

main advantage of this procedure is the spontaneous

adaptation to variability changes in the series, which

enables a more reliable removal of artifacts and

VPCs. This new filtering algorithm consists of three

sub-procedures: (i) the removal of obvious recogni-

tion errors, (ii) the adaptive percent filter, and (iii)

the adaptive controlling filter. A MATLAB imple-

mentation of the preprocessing algorithm is avail-

able from <tocsy.agnld.uni-potsdam.de>.

3.2 Heart rate and Blood Pressure

Variability

Standard methods of HRV analysis include time and

frequency domain parameters; these are linear meth-

ods. Time domain parameters are based on simple

statistical methods derived from the RR-intervals as

well as the differences between them. The mean

heart rate is the simplest parameter, but the standard

deviation over the whole time series (sdNN) is the

most prominent HRV measure for estimating overall

HRV. A list of these parameters is given in Table 1.

These parameters can be calculated for short (5 min-

utes) and long (24 hours) term epochs, representing

short-term and long-term variability, respectively, or

for averaged short-term epochs (e.g. a mean of 288

five-minute intervals a day). The overall HRV esti-

mate, sdNN, and other time domain parameters can

be used to predict mortality in the recovery period

after myocardial infarction. In one of the first risk

studies using these parameters, (Kleiger, 1987)

showed that an sdNN<50ms was associated with a

5.3-fold increased mortality when compared to pa-

tients with preserved HRV (sdNN>100ms).

Time domain geometric methods (see Table 1)

are methods by which the NNIs are converted into

special geometric forms quantifying their distribu-

tion. Special forms are used to make the approach

more insensitive to artifacts and ectopic beats. A

disadvantage of these methods is that they require a

considerable number of RR-intervals; they are thus

not applicable to very short-term time series. A tri-

angular index, HRVi, showing reduced HRV has

been associated with both arrhythmic and non-

arrhythmic death (Task Force, 1996).

Table 1: Description of time and frequency domain pa-

rameters, adopted standards (Task Force, 1996) and addi-

tional measures developed by the authors (•). NNI stands

for the filtered beat-to-beat intervals (NN-intervals).

Variable Units Definition

Time domain statistical methods

meanNN ms/mm Hg

Mean NNI and mean BP, re-

spectively

sdNN ms/mm Hg

Standard deviation of all NNI

and BP values, respectively

rmssd ms/mm Hg

Root mean square of succes-

sive NNI/BP differences

•pNNX %

Percentage of beat-to-beat

differences greater than X

ms/mm Hg (e.g. X = 3/6/9

ms/mm Hg)

•pNNlX %

Percentage of beat-to-beat

differences lower than X

ms/mm Hg (e.g. X = 3/6/9/12

ms/mm Hg)

•Shannon None

Shannon entropy of the histo-

gram (density distribution of

the NNIs/ BP values)

•RenyiX None

Renyi entropy of the order X of

the histogram (e.g. X =

2/4/0.25)

Time domain geometric methods

HRVi none

HRV triangular index (see

(Task Force, 1996) for details)

TINN ms

Baseline width of the minimum

square difference triangle

Frequency domain methods

P ms

/mm Hg

Total power from 0 − 0.4Hz

VLF ms

/mm Hg

Very low frequency band,

0.0033 − 0.04Hz

LF ms

/mm Hg

Low-frequency band, 0.04 −

0.15 Hz

HF ms

/mm Hg

High-frequency band, 0.15 −

0.4 Hz

LF/HF None Quotient of LF and HF

LFn None

Normalized low-frequency

band (LF/(LF+HF))

cross 1/f

Intercept of a log-log-power

spectrum

slope 1/f

Slope of a log-log-power spec-

trum

ANALYSIS OF HEART RATE AND BLOOD PRESSURE VARIABILITY IN PREGNANCY - New Method for the

Prediction of Preeclampsia

We introduced a more robust method to quantify

the distribution (Voss, 1996) based on information

theory, in particular the Shannon and the Renyi en-

tropies of the histogram. We demonstrated the use-

fulness for risk stratification in a blinded study two

years later (Voss, 1998). Frequency domain HRV

parameters allow periodic dynamics in the heart rate

time series to be analyzed (Akselrod, 1981). There

are mainly two different techniques for spectral

analysis: methods based on Fast Fourier Transform

(FFT) and parametric autoregressive model esti-

mates of wavelet approaches. The results obtained

from using different spectral methods should be

comparable though (apart from differences in time

and frequency resolution). The Task Force on HRV

(Task Force, 1996) recommended that power spec-

tral analysis of 5-minute ECG recordings be used to

assess autonomic physiology and pharmacology.

Very low, low and high frequencies (see Table

1) can be estimated from such 5-minute ECG re-

cordings. In this study, all frequency domain pa-

rameters were calculated from the complete 30-

minute recording.The high frequency power reflects

the modulation of vagal activity by respiration

whereas the low-frequency power represents vagal

and sympathetic activities via the baroreflex loop.

The low-to-high frequency ratio is used as an index

of sympathovagal balance (Malliani, 1991). The

suitability of frequency domain parameters for risk

stratification of post-infarction patients was proven

by Bigger et al. (Bigger, 1992) - a reduction in ultra

low and very low frequency power is associated

with pathologies.For blood pressure series, all HRV

parameters described can be calculated accordingly,

only some statistical parameters need to be adapted

(e.g. pNN50 makes no sense for BPV - the standard

deviation for BP series varies between 5 and 10

mmHg).

3.3 Baroreflex Sensitivity

Analysis of spontaneous baroreflex sensitivity

(BRS) is very important for cardiac risk stratification

of various cardiovascular diseases (La Rovere,

2001). BRS slope is defined as the instinctive

change of NNI related to increasing or decreasing

levels of systolic blood pressure and is expressed in

[ms/mmHg]. There is evidence showing that a de-

creased BRS may carry an adverse prognosis in car-

diac patients (La Rovere, 1998).

For several years, BRS was determined pharma-

cologically (phenylephrine, nitro-prusside) (Vanoli,

1994) or mechanically (Cohen, 1981) until, in the

1980s, innovative methods of estimating BRS were

developed based on spontaneous heart rate and

blood pressure fluctuations (Di Rienzo, 1985).

Figure 1: The Dual Sequence Method of estimating spon-

taneous BRS analyses simultaneous (sync) and delayed

responses (shift 3, variable delay) of heart rate to blood

pressure increases (a) as well as bradycardic and tachy-

cardic blood pressure fluctuations (classical sequence

method (b). Moreover, also the slope sector distribution is

quantified (c). These slope sectors also can be defined as

overlapping regions.

These methods evaluate arterial baroreflex func-

tion in the absence of external stimulations of the

cardiovascular system, therefore defined as “sponta-

neous”. These spontaneous techniques nowadays are

the state of the art in research, though not in clinical

practice. We introduced the Dual Sequence Method

(DSM) (Malberg, 2002) for advanced spontaneous

baroreflex sensitivity estimates. This method con-

siders not only bradycardic (blood pressure increase

causes RR-interval increase) and tachycardic (blood

pressure decrease causes RR-interval decrease)

blood pressure fluctuations as introduced in the se-

quence method (Di Rienzo, 1985) (see Figure 1 (a)),

but also defines slope sectors quantifying the BRS

slope distribution (see Figure 1 (b)). Earlier studies

showed that the heart rate does not simultaneously

respond to the blood pressure fluctuation (Manicia,

1985). Therefore, DSM quantifies synchronous as

well as delayed heart rate response to the same BP

fluctuation (see Figure 1 (c)).

In summary, these are the parameter blocks and

ranges calculated by DSM:

(i) the total number of slopes in different sec-

tors within the time series,

(ii) the percentage of slopes relative to the to-

tal number of slopes in different sectors,(iii) the

numbers of bradycardic and tachycardic slopes,

(iv) the shift operation from the first to the third

heart beat triples, a variable lag, and

(v) the average slope of all fluctuations and its

standard deviation.

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

The average BRS slope is defined as the NNI

difference related to SBP changes, and is estimated

by linear regression.

The parameters, ‘P_brady’, and, ‘P_tachy’, char-

acterize the incidence of increasing and decreasing

SBP triples with regard to the total number of SBP

values. Consequently, these parameters estimate the

basic cause of BRS activity. A reduced number of

ramps in SBP unavoidably leads to a reduced num-

ber of HR responses. The parameters are defined as

P_brady = (No. of increasing SBP triples/

total No. of SBP triples) * 100%

P_tachy = (No. of decreasing SBP triples/

total No. of SBP triples) * 100%

The percentage of adequate HR responses (BRS

events) relative to the numbers of SBP ramps is de-

scribed by the ‘Activation’ parameter. It is defined

Activation = (No. of BRS events/

No. of SBP ramps) * 100%.

In contrast to classical BRS methods, DSM de-

fines slope sectors allowing to quantify the BRS

slope distribution. Sectors with a range of 2 ms/mm

Hg have been proven to act as a suitable partition in

patient studies. Then, the percentages of BRS events

in different slope sectors relative to the total number

of BRS can be estimated. Moreover, the total num-

ber of BRS events is normalized to the mean heart

rate. For detailed definitions of the DSM parameters,

reference is made to the original contribution (Mal-

berg, 2002). These parameters are calculated for

bradycardic as well as tachycardic fluctuations, both

synchronous or delayed, to analyze a possibly de-

layed response of the heart rate to the same blood

pressure oscillation. This DSM method is used to

quantify sequences of length three; longer sequences

turned out not to be useful for spontaneous BRS

estimates because of their low occurrence.

3.4 Statistics

In this study, the Kruskal-Wallis test was used to

determine intergroup differences in clinical parame-

ters. The Mann-Whitney U test was employed to

analyze the differences in variability parameters

among pregnant women with uterine perfusion dis-

orders developing PE (number = 24) compared to

those not developing PE (NoPE, number = 72). The

level of significance of the intergroup differences

was defined as p < 0.05. Due to the explorative

character of the study we did not apply the multiple

test correction. Stepwise discriminant analysis was

employed to determine the best combinations of

parameters for separating individual groups.

4 RESULTS

In this study, no pregnant woman with normal uter-

ine perfusion developed hypertensive pregnancy-

related disorders. In the period between the 18th and

the 26th weeks of gestation, in the abnormal uterine

perfusion group, the following differences were

found by variability analysis (Tables 2 – 3). In HRV

analysis, both the mean and the standard deviation

were unchanged, while some frequency domain pa-

rameters showed significant differences. Interest-

ingly, all significant parameters point to very low

frequencies below 0.04 Hz.

As in the HRV analysis, the mean values and the

standard deviation were unchanged also in SBPV

and DBPV. On the other hand, especially in DBP,

time domain and frequency domain parameters as

well as non-linear parameters showed significant

differences. The most prominent difference was

found to be the ‘high frequency’ in diastolic blood

pressure.

Table 2: HRV analysis in the 18th – 26th weeks of gesta-

tion in pregnant women with abnormal Doppler findings

developing either PE or NoPE after the 30th week of ges-

tation.

NoPE PE P value

meanNN 759.8±104.4 755.3±113.4 n.s.

sdNN 44.7±16.2 49.0±18.1 n.s.

VLF 10.18±11.45 13.46±11.46 0.013

VLF/P 0.35±0.11 0.44±0.10 0.005

ULF/P 0.21±0.15 0.14±0.08 0.029

cross 1/f 1.79±1.62 3.09±1.48 <0.001

slope 1/f -0.77±0.56 -0.38±0.51 0.002

Table 3: Analysis of systolic and diastolic blood pressure

variability in the 18th – 26th weeks of gestation in preg-

nant women with abnormal Doppler findings developing

either PE or NoPE after the 30th week of gestation.

SBPV NoPE PE P value

meanNN 122.4±16.1 128.6±13.2 n.s.

sdNN 7.81± 2.03 8.36±1.86 n.s.

Rmssd 2.66±0.56 3.02±0.81 n.s.

pNN2 0.27±0.11 0.34±0.13 n.s.

LF 0.14±0.09 0.16±0.07 n.s.

HF 0.03±0.02 0.05±0.03 0.021

WPSUM02 0.46±0.16 0.43±0.11 n.s.

PLVAR2 0.031±0.045 0.014±.0171 n.s.

DBPV NoPE PE P value

meanNN 68.0±11.2 72.9±9.0 n.s.

sdNN 4.15±1.07 4.74±1.43 n.s.

Rmssd 1.89±0.33 2.18±0.59 0.033

pNN2 0.13±0.06 0.20±0.11 0.012

LF 0.05±0.03 0.07±0.04 0.011

HF 0.01±0.01 0.02±0.01 0.002

WPSUM02 0.47±0.14 0.41±0.13 0.049

PLVAR2 0.110±0.083 0.080±0.114 0.004

ANALYSIS OF HEART RATE AND BLOOD PRESSURE VARIABILITY IN PREGNANCY - New Method for the

Prediction of Preeclampsia

Also BR regulation as characterized by DSM

showed differences in pregnant women developing

PE compared to women without PE (Table 4).

Analysis reveals that the number of rises in SBP

potentially initiating BR increases significantly in

PE.

Table 4: Baroreflex analysis by the dual sequence tech-

nique in the 18th – 26th weeks of gestation in pregnant

women with abnormal Doppler findings developing either

PE or NoPE after the 30th week of gestation.

Bradycardic BR Regulation

Parameters NoPE PE P

No. of SBP

ramps

458.6±94.1 528.1±128.5 0.005

No. of SBP

ramps [%]

18.5±2.6 21.1±4.0 <0.001

BR time delay

[beats]

1.7±0.4 1.8±0.3 n.s.

No. of slopes

between 4-6

ms/mm Hg

35.4±13.6 46.6±22.2 0.019

No. of slopes

between 3-5

ms/mm Hg

36.5±15.2 49.1±20.4 0.004

No. of slopes

between 5-7

ms/mm Hg

32.2±12.3 41.1±19.2 n.s.

Total No. of BR

slopes

173.9±50.0 216.7±77.9 0.009

average BR slope

[ms/mm Hg]

13.3±5.6 13.2±5.6 n.s.

BR Activation

[%]

38.3±8.7 41.3±10.9 n.s.

Tachycardic BR Regulation

Parameters NoPE PE P

No. of SBP

ramps

464.9±106.3 527.0±97.3 0.008

No. of SBP

ramps [%]

18.8±3.4 21.2±2.7 0.005

BR time delay

[beats]

1.5±0.3 1.7±0.5 0.029

No. of slopes

between 4-6

ms/mm Hg

41.9±19.3 60.6±20.6 <0.001

No. of slopes

between 3-5

ms/mm Hg

47.0±22.7 65.3±24.8 0.003

No. of slopes

between 5-7

ms/mm Hg

38.4±17.4 50.2±17.6 0.004

Total No. of BR

slopes

200.0±59.1 242.0±64.6 0.005

average BR slope

[ms/mm Hg]

12.9±5.6 12.2±6.1 n.s.

BR Activation

[%]

43.5±9.4 46.5±11.5 n.s.

Nevertheless, BR per se does not change in its

response to activation and mean rise. Also, the total

number of BR fluctuations and the number in the

low regulation segment (between 3 and 7 ms/mm

Hg) is elevated in PE. In the tachycardic range, BR

regulation also starts later in the PE group than in

the NoPE group.

The application of stepwise discriminant analysis

selected the following three parameters as the best

parameters predicting PE: HRV: VLF/P, DBP: HF,

BR: number of tachycardic slopes between 4-6

ms/mm Hg. A sensitivity and a specificity of 87.5

%, a positive predictive accuracy of 70.0 % were

found with a negative predicted accuracy of 95 %.

Interestingly, these are the same parameters which

had been found in our previous study (Walther,

2006).

Figure 2: Example of the change in variability measure

during pregnancy in the case of PE genesis. The increase

in the high-frequency component of diastolic blood pres-

sure is shown.

Figure 2 shows an example of the change over

time of variability measure in the course of preg-

nancy in patients developing PE as against pregnant

women without this development.

5 DISCUSSION

Early prediction of PE is one of the most important

challenge in obstetrics. Establishing a simple test

manageable under clinical conditions is a major

challenge. Doppler sonography or combined with

humoral or endothelial parameters either attained

low sensitivity / sufficient sensitivity or a low posi-

tive predictive value and are very costly or even in-

vasive.

Although BRV, BPV, and BRS have been estab-

lished in cardiology for risk stratification, their use

for early detection of hypertensive pregnancy disor-

ders is still very rare. As various cardiovascular dis-

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

eases can be predicted by a gradual change in car-

diovascular regulation, the approach used in this

study also was to cover the genesis of PE. This study

is aimed at investigating alterations in HRV, BPV

and BRS to predict the sudden steep increase of

blood pressure which is caused by PE.

With an incidence of 3 – 5 % of all pregnancies

in the Western population, pathological uterine per-

fusion in the second trimester is known as an indi-

rect sign of inadequate trophoblasts. The positive

predicted value of this study, however, is only

around 30 %. In an earlier study of the variability

analysis of PE, the authors were able to show that

the use of only the variability parameter was able to

raise to 50 % the positive predictive value, which

does represent the highest possible PPA, but is not

yet sufficient for clinical routine screening. Except

for that, the three variability parameters described

above attained the highest PPA of any one method

of examination which, in addition, is independent of

humoral factors and other single clinical parameters.

This study has shown that the combination with

Doppler sonography of uterine arteries confirms the

highest possible PPA as compared to all published

non-invasive trials (Walther, 2006). The PPA of

approx. 70% is indicative of the clinical relevance of

Doppler examination combined with variability

analysis. However, due to the exploratory design of

this study, these parameters need to be validated

prospectively - especially in connection with hu-

moral factors. Anyway, the same three parameters,

which had been found in our previous study

(Walther, 2006), were selected in the discriminant

analysis. These parameters obtained nearly the same

classification results – which is already a first vali-

dation.

On the basis of the variability measures deter-

mined, the following interpretation could be possible

in the course of pregnancy for the genesis of PE in a

cardiovascular model (see Figure 3).

Figure 3: Connections between the heart rate, NNI, the

cardiac output, CO, and the blood pressure, BP, in

autonomous regulation of the cardiovascular system,

modified from [Saul, 1991)

In Figure 3 a simplified model of the cardiovascular

circulatory system is presented. Mediated by the

electromechanical coupling, the NNIs initiate a car-

diac output (CO) of the heart. The resulting blood

pressure in the periphery is influenced by this CO

and by the vascular system. In the feedback the

NNIs are influenced by the BRS via sympathetic and

parasympathetic activation. Additionally, the total

system is superimposed by mechanical and neuronal

influences of the respiration.

Our analyses point to a more vascular disorder as

the cause of the cardiovascular alteration. Obvi-

ously, the heart plays only a secondary role. Appar-

ently, there is an early pathological modification in

vessel behavior measurable already in the Doppler

sonogram. However, this parameter obtains a low

PPA only. This incipient endothelial dysfunction,

which is still very weak in the 18th – 26th weeks of

gestation, has no influence on the mean values of

diastole, systole, and heart rate. On the other hand,

especially the variability of blood pressure seems to

change as a consequence of continuing pathological

arterial stiffness in so far as the minor fluctuations in

blood pressure become more pronounced as a result

of the decreasing windkessel function of vessels.

This can be represented by the changes in blood

pressure variability (see parameters: SBPV: ‘HF’,

DBP: ‘Rmssd’, ‘pNN2’, ‘LF’, ‘HF’, ‘WPSUM02’,

‘PLVAR2’, BRS: ‘No. of SBP ramps’, ‘No. of SBP

ramps [%]’). The baroreflex reacts more strongly to

this change, i.e. it reacts more frequently to these

slight, but more frequent blood pressure stimuli (see

BR parameters: ‘No. of slopes between 4-6 ms/mm

Hg’, ‘No. of slopes between 3-5 ms/mm Hg’, ‘No.

of slopes between 5-7 ms/mm Hg’, ‘Total No. of BR

slopes’). The baroreflex function (i.e. the intensity of

response of the BR) seems to be completely un-

changed (see BR parameters: ‘average BR slope’,

‘BR activation’). The changes in BPV and BR are

continued in HRV either as a consequence of the

counter regulation of the heart rate responding to

blood pressure fluctuations, or due to other regula-

tory influences modulated by respiratory sinus ar-

rhythmia (see HRV parameters: ‘VLF’, ‘VLF/P’,

‘ULF/P’, ‘cross 1/f’, ‘slope 1/f’). Thus, for example,

the increase in diastolic high frequency, which is

modulated by respiratory sinus arrhythmia, may re-

flect early pathological arterial stiffness. This leads

to the undamped, respiration-induced pulse-wave

oscillations detected by our method. This is congru-

ent with the hypothesis that patients later developing

PE are characterized by early pathological modifica-

tions in vessel behavior. The physiological conclu-

sion could be drawn that variability analysis meas-

ANALYSIS OF HEART RATE AND BLOOD PRESSURE VARIABILITY IN PREGNANCY - New Method for the

Prediction of Preeclampsia

ures the consequences to blood pressure, to the in-

teraction between blood pressure and heart rate, and

to the heart rate of incipient endothelial dysfunction,

which is not thought to be sufficient to predict PE

solely on the basis of the Doppler sonogram.

In a previous methodological study the applica-

bility of different BR methods was tested (Vanoli,

1994, Laude, 2004). All described methods estimate

only the average BRS slope. The DSM however, is

able to obtain additional insights into the cardiovas-

cular regulation. In this study, ‘average slope’ is not

altered, however more sophisticated DSM parame-

ters found high significant differences. So we con-

clude, that the parameter ‘average slope’ is not suffi-

cient for PE prediction. The best discrimination had

been obtained by the combination of non-linear BR

parameters and linear HRV und BPV parameters.

In summary, it can be said that examination of

uterine perfusion combined with the characterization

of cardiovascular regulation in the second trimester

has achieved the most accurate prediction of PE sev-

eral weeks before its clinical manifestation so far. In

this application, the biosignal analysis emphasizes

its importance as a non-invasive, cheap and univer-

sal diagnostic approach. This opens up potential

therapeutic strategies for suppressing pathophysi-

ological symptoms of the disease to further decrease

maternal and neonatal morbidity and mortality rates.

REFERENCES

Chien, P.F., Arnott, N., Gordon, A., Owen, P., Khan, K.S.

2000, How useful is uterine artery Doppler flow

velocimetry in the prediction of pre-eclampsia, intrau-

terine growth retardation and perinatal death? An

overview. Brit J Obstet Gynaecol 107:196-208.

Benedetto, C., Valensise, H., Marozio, L., Giarola, M.,

Massobrio, M., Romanini, C. 1998, A two-stage

screening test for pregnancy-induced hypertension and

preeclampsia. Obstet Gynecol 92:1005-1011.

La Rovere, M.T., Pinna, G.D., Hohnloser, S.H., Marcus,

F.I., Mortasa, A., Nohora, R., Bigger, T., Camm, A.J.,

Schwartz, P.J. 2001, Baroreflex sensitivity and heart

rate variability in the identification of patients at risk

for life-threatening arrhythmias. Circulation 103:

2072–2077.

Walther, T., Wessel, N., Baumert, M., Stepan, H., Voss,

A., Faber, R. 2005, Longitudinal analysis of heart rate

variability in chronic hypertensive pregnancy. Hyper-

tension Res 28:113-118.

Hermida, R.C., Ayala, D.E., Mojon, A., Fernandez, J.R.,

Silva, I., Ucieda, R., Iglesias, M. 1998, Blood pressure

excess for the early identification of gestational hyper-

tension and preeclampsia. Hypertension 31:83-89.

Faber, R., Baumert, M., Stepan, H., Wessel, N., Voss, A.,

Walther, T. 2004, Baroreflex sensitivity, heart rate and

blood pressure variability in hypertensive pregnancy

disorders. J Hum Hypertens 18:707-712.

Walther, T., Wessel, N., Malberg, H., Voss, A., Stepan,

H., Faber, R. 2006, A combined technique for predict-

ing pre-eclampsia: concurrent measurement of uterine

perfusion and analysis of heart rate and blood pressure

variability. Journal of Hypertension 24:747-750.

Kleiger, R.E., Miller, J.P., Bigger, T., Moss, A.J. 1987,

Decreased heart rate variability and its association

with increased mortality after acute myocardial infarc-

tion. Am J Cardiol 59:256–262.

Lippman, N., Stein, K.M., Lerman, B.B. 1994, Compari-

son of methods for removal of ectopy in measurement

of heart rate variability. Am J Physiol 267:H411-418.

Wessel, N., Voss, A., Malberg, H., Ziemann, Ch., Voss,

H.U., Schirdewan, A., Meyerfeld, U., Kurths, J. 2000

Nonlinear analysis of complex phenomena in cardi-

ological data. Herzschr Elektrophys 11: 159-173

Task Force of the European Society of Cardiology and the

North American Society of Pacing and Electrophysi-

ology. 1996, Heart rate variability: standards of meas-

urement, physiological interpretation and clinical use.

Circulation 93:1043-1065.

Voss, A., Kurths, J., Kleiner, H.J., Witt, A., Wessel, N.,

Saparin, P., Osterziel, K.J., Schurath, R., Dietz, R.

1996, The application of methods of non-linear dy-

namics for the improved and predictive recognition of

patients threatened by sudden cardiac death. Cardio-

vasc Res 31:419–433.

Voss, A., Hnatkova, K., Wessel, N., Kurths, J., Sander, A.,

Schirdewan, A., Camm, A.J., Malik, M. 1998, Multi-

parametric analysis of heart rate variability used for

risk stratification among survivors of acute myocardial

infarction. Pacing Clin Electrophysiol 21(1 Pt 2):186–

192.

Akselrod, S., Gordon, D., Ubel, F.A., Shannon, D.C.,

Barger, A.C., Cohen, R.J. 1981, Power spectrum

analysis of heart rate fluctuation: A quantitative probe

of beat-to-beat cardiovascular control. Science

213:220–222.

Malliani, A., Pagani, M., Lombardi, F., Cerutti, S. Cardio-

vascular neural regulation explored in the frequency

domain. Circulation1991;84:482–92.

Bigger Jr, J.T., Fleiss, J.L., Steinman, R.C., Rolnitzky,

L.M., Kleiger, R.E., Rottman, J.N. 1992, Frequency

domain measures of heart period variability and mor-

tality after myocardial infarction. Circulation 85:164–

71.

La Rovere, M.T., Bigger, J.T., Marcus, F.I., Mortara, A.,

Schwartz, P.J. 1998, Baroreflex sensitivity and heart-

rate variability in prediction of total cardiac mortality

after myocardial infarction. atrami (autonomic tone

and reflexes after myocardial infarction) investigators.

Lancet 351(9101):478–484.

Vanoli, E., Adamson, P.B. 1994, Baroreflex sensitivity:

methods, mechanisms, and prognostic value. Pacing

Clin Electrophysiol 17(3 Pt 2):434–445.

BIOSIGNALS 2008 - International Conference on Bio-inspired Systems and Signal Processing

Cohen, I.M., O’Connor, D.T., Preston, R.A., Stone, R.A.

1981, Long-term clonidine effects on autonomic func-

tion in essential hypertensive man. Eur J Clin Phar-

macol 19(1):25–32.

Di Rienzo, M., Bertinieri, G., Mancia, G., Pedotti, A.

1985, A new method for evaluating the baroreflex role

by a joint pattern analysis of pulse interval and systolic

blood pressure series. Med Biol Eng Comput 23(suppl

I):313-314.

Malberg, H., Wessel, N., Schirdewan, A., Hasart, A., Os-

terziel, K.J., Griessbach, G., Voss, A. 2002, Advanced

analysis of the spontaneous baroreflex sensitivity us-

ing the Dual Sequence Method. Clinical Science 102:

465-473.

Saul, J.P., Berger, R.D., Albrecht, P., Stein, S.P., Chen,

M.H., Cohen, R.J. 1991, Transfer function analysis of

the circulation: unique insights into cardiovascular

regulation. Am J Physiol 261: H1231-H1245.

Laude, D., Elghizi, J.L., Girard, A., Bellard, E., Bouhaddi,

M., Castiglioni, P., Cerutti, C., Cividjan, A., Di Ri-

enzo, M., Fortrat, J.O., Janssen, B., Karemaker, J.M.,

Lefthériotis, G., Parati, G., Persson, P.B., Porta, A.,

Quintin, L., Regnerd, J., Rüdiger, H., Stauss, H.M.

2004, Comparison of various techniques to estimate

spontaneous baroreflex sensitivity (the EuroBaVar

study). Am J Physiol Regul Integr Comp Physiol, 286:

R226-231

ANALYSIS OF HEART RATE AND BLOOD PRESSURE VARIABILITY IN PREGNANCY - New Method for the

Prediction of Preeclampsia