classification system for fetal heart rate variability measures … · 2013. 11. 27. · university...

Classification System for Fetal Heart Rate

Variability Measures Based on

Cardiotocographies

João A. L. Marques University Lusiada of Angola/Department of Informatics, Lobito, Angola

University of Leicester/Department of Engineering, Leicester, United Kingdom

Email: [email protected]

Paulo C. Cortez and João P. V. Madeiro Federal University of Ceará/Department of Engineering of Teleinformatics, Fortaleza, Brazil

Email: {cortez, joaopaulo}@deti.ufc.br

Fernando S. Schlindwein University of Leicester/Department of Engineering, Leicester, United Kingdom

Email: [email protected]

Abstract—The Fetal Heart Rate interpretation based on

Cardiotocographies (CTG) is the most common practice of

obstetrician medical staffs. Computerized CTG Systems are

used with the aim to reduce subjective aspects of these

diagnostics. The Fetal Heart Rate Variability (FHRV)

analysis using the CTG signal is an unusual approach. This

work proposes a FHRV analysis based on the evaluation of

time domain parameters (statistic measures); frequency

domain parameters; and the short and long term variability

obtained from the Poincaré plot. A normal distribution is

presumed for each parameter and a normality criterion is

proposed. Specific and overall classifications are proposed

to help improve the fetal conditions interpretation,

expanding the conventional FHR analysis.

Index Terms—fetal heart rate variability (FHRV),

cardiotocography (CTG), diagnostic

I. INTRODUCTION

The cardiologic and autonomic nervous systems (ANS)

continuously look for a dynamic balance where the

parasympathetic and sympathetic systems act as opposite

forces influencing the heart rhythm modulation. The first

one increases the heart rate and decreases the variability

while the second system does the opposite action [1].

The Fetal Heart Rate Variability (FHRV) can be

obtained by the Cardiotocography (CTG), as it is

considered a gold standard exam for the detection of fetal

heart rate (FHR). The Doppler sensor has similar

accuracy when compared with the abdominal ECG for

the fetal heart beat detection [2].

The CTG records continuously and simultaneously the

FHR and the uterine tonus (for uterine contractions

monitoring). Fetal movements can also be recorded

Manuscript received June 3, 2013; revised August 25, 2013.

manually by the mother. These monitoring allow the

detection of a large set of diseases or changes in the fetal

health status [3].

Usually, the CTG exam is done in risky pregnancies

because fetal distress can be earlier detected. Depending

on the situation, the exam is applied before labour, period

of time named as antepartum, and also during labour, the

intrapartum period [4].

Previous works are using the FHVR analysis acquiring

the fetal ECG Signal. Lebrun (2003) states that the FHRV

analysis during the last trimester can provide important

clinical information after birth [5]. A fetal development

indice based on time and frequency parameters is

suggested based on the FHR decrease and variability

increase during the pregnancy.

Sibony et al. (1994) present that the FHRV can be used

to detect the fetal status also during labour [6]. They

propose the identification of new frequency intervals and

two evaluation criteria based on the FHRV spectral

analysis. Other works consider the frequency domain

parameters as part of an overall comparison with other

monitored systems to determine fetal status [7].

The time domain HRV analysis considers geometric

and statistic approaches [8]. The geometric metrics are

based on the histogram of the set of normal intervals

between QRS complexes. In statistical analysis there are

several metrics divided in three groups. Each metric is

defined in Table I. The first one evaluates the heart rate

behaviour as a whole, i.e., considers the whole set of

samples for its calculations. The metrics are the SDNN

and the SDANN. The RMSSD measure considers the

interval between heart beats and belongs to the second

group and reflects the high frequency characteristics of

the signal. Finally, the long term variability (LTV) and

short term variability (STV) are obtained from the

Poincaré plot.

2013 Engineering and Technology Publishing

Journal of Life Sciences and Technologies Vol. 1, No. 3, September 2013

184doi: 10.12720/jolst.1.3.184-189

The parasympathetic stimulation results in a fast and

short term answer in the heart beats, affecting

immediately the interval between them. It can be

evaluated when considering parameters such as RMSSD.

The sympathetic stimulation is slower with a latency

period that can vary from 5 to 20 seconds [1]. Parameters

considering many RR intervals, such as SDNN and

SDANN are used to evaluate both systems as a whole.

TABLE I. HRV MEASURES CONSIDERED AS CLASSIFICATION

CRITERIA

Measure Unit Description

SDNN ms Standard deviation considering

all NN intervals

SDANN ms Mean of all standard deviations of 5-minutes

segments of NN.

RMSSD ms The square root of the mean squared difference of

successive NNs

LF ms2 Low frequency power (0,04 – 0,15 Hz)

HF ms2 High frequency Power

(0,15 – 0,40 Hz)

LF/HF -- LF and HF high frequency ratio

The frequency domain parameters usually considered

in heart rate variability analysis are divided into

frequency intervals, such as Ultra Low Frequency (ULF),

Very Low Frequency (VLF), Low Frequency (LF) and

High Frequency (HF) [1]. In this work we consider only

the LF and HF intervals. The HF component corresponds

to changes in the heart rate related with the respiratory

cycles, which are tipically managed by the

parasympathetic system. On the other hand, the LF

component is influenced by both systems.

Actually, the HRV in time and frequency domain are

different expressions of the same phenomenon, some

correlation among those parameters can be demonstrated.

The SDNN parameter for example is related to the total

power of the spectral analysis. The time domain RMSSD

is correlated with the high frequency component in the

frequency domain since it considers the difference

between two RR adjacent intervals, quantifying fast

changes of the heart rate. Another example is the

correlation between the SDANN and the ULF frequency

band.

This work presents a FHRV analysis based on the

signal obtained by the CTG examination. Classification

criteria for the FHRV parameters and for the examination

as a whole are proposed.

II. MATERIALS AND METHODS

A. Database and Development Environment

The Matlab software version 7.6.0.324 R2008a is used

as the development environment [9]. The whole set of

HRV parameters (time and frequency domain) and the

STV and LTV based on Poincaré plot were calculated

using the system proposed by Madeiro [10].

The results were obtained from one previously

identified database from the Trium Analysis Online

GmBH, in Munich, Germany. The database is identified

as CTG-A, and has 80 examinations in antepartum period

of time, i.e., before labour, with gestational age varying

from the 28th

to the 34th

week.

From these, 58 examinations are classified as control

(normal fetal and low level of suspicious status) and 22 as

study (high level of suspicious or pathological). There are

no uterine contractions and the occurrence of FHR

accelerations may indicate normality.

B. FHRV Analysis

A block diagram with all the steps to perform the FHR

variability analysis is presented in Fig. 1. After the time

and frequency domain parameters are determined, the

long and short term variability can be obtained from the

Poincaré plot.

Figure 1. The block diagram of FHRV analysis

After calculating several different parameters, this

work considers the subset defined in Table I. The

frequency ranges considered in this work are also

presented. The LF and HF are expressed in normalized

units. The LF/HF ratio is considered in all the results

because it shows the balance between the sympathetic

and parasympathetic systems.

C. Classification Criteria

There are no previously determined normality criteria

for the FHRV. This work presents a classifier based on a

set of criteria.

Considering a normal statistical distribution for each of

the chosen parameters (Pi), the following criteria are

considered in this analysis:

Normality:

µPi - σPi ≥ Pi ≤ µPi + σPi

Suspicious:

µPi + σPi < Pi ≤ µPi + 2(σPi)

µPi - 2(σPi) ≤ Pi < µPi - σPi

Abnormality:

Pi > µPi + 2(σPi)

Pi < µPi - 2(σPi)

where µPi is the mean and σPi is the standard deviation for

each parameter Pi.

For the overall classification of the exam, four

different possibilities are considered:

If all parameters are classified as normal, then the

exam is considered as “FRHV Normality”.



185

If one parameter is suspicious then the exam is

labeled as “Attention”.

If two or more are suspicious, then the label is

“Suspicious”.

If any parameter is classified as abnormal, the

exam is classified as “FRHV Abnormality”.

It is important to notice that these criteria are not

comparable with the conventional classification and is

proposed to act as a complementary tool. The

conventional analysis can find pathologies where the

FHR does not and vice versa.

III. RESULTS AND DISCUSSION

In this section, the results for all of the parameters is

presented, such as for the time domain or the frequency

domain. The mean µ, the standard deviation σ, the

maximum value Max, the minimum value Min and the

Pearson variability are presented in Table II and Table III.

TABLE II. HRV MEASURES CONSIDERED AS CLASSIFICATION

CRITERIA

Measure SDNN SDANN RMSSD

µ 31.80 22.13 3.36

σ 10.81 8.67 0.96

Max 81.18 53.61 8.25

Min 15.07 5.05 1.93

Pearson

Variability 34.01 39.20 28.71

TABLE III. HRV MEASURES CONSIDERED AS CLASSIFICATION

CRITERIA

Measure SDNN SDANN RMSSD

µ 31.80 22.13 3.36

σ 10.81 8.67 0.96

Max 81.18 53.61 8.25

Min 15.07 5.05 1.93

Pearson

Variability 34.01 39.20 28.71

The Pearson Variability indicates that in this database

there are significant variations in almost the whole set of

parameters, especially when analysing the time domain

parameters. This indice is also high for the ratio LF/HF.

For a better comprehension of how the parameters are

related to each other the correlation coefficient ρ are also

calculated. For the long and short term variability

parameters, STV and LTV, is found ρ = 0.5739, showing

a strong correlation between them. For the SDNN and

SDANN parameters there is a stronger correlation, with ρ

= 0.6984. For the RMSSD parameter, which is by

definition, related with high frequency components, there

is a correlation with the HF parameter, ρ = 0.4706.

After these preliminary results, the detailed

classification process for each of the parameters is then

performed, assuming that all of them follow a normal

distribution.

In Fig. 2, the LF/HF values are plotted classified as

normal, suspicious and abnormal. The results according

to the normality classification contain the most part of the

exams, while only three were considered abnormal.

Figure 2. LF/HF classification

Figure 3. Varaiability classifications: (a) LTV and (b) STV

The scatter plot for the variability parameters, LTV

and STV, are presented in Fig. 3 (a) and Fig. 3 (b). There

are two significant outliers in both plots. These exams

must be carefully analysed as they may indicate fetal

distress.

Finally, the time domain statistics are presented in Fig.

4 (a), (b) and (c). As also shown in other previous

graphics, there are only a few abnormal and a significant

number of suspicious classification. Besides, there are not

abnormal exams under the lower suspicious values.

After this classification based on each parameter, the

overall analysis of each examination is performed. There

are four different outputs and the “Suspicious” was the

most common classification with 35% of the occurrences

while 21% were classified with the “Attention” label.

This means that 56% of the whole examinations set had

at least one FRHV parameter out of the normality

classification criteria. There were 30% of “Normal” and

14% of “Abnormal” outputs. These results are presented

in Fig. 5.

A group of four exams previously classified as normal

are presented in Table IV. All of them are also classified



186

as normal for the proposed classification using the FHRV

parameters. For these exams, there is a match between the

visual analysis and the FHRV analysis, considering the

normality classification proposed in this work. In a

general point of view, there are 17 exams among 58 with

this match of classification. Also, eight exams previously

classified as normal received the “Attention” label.

Figure 4. Time domain classifications: (a) SDNN, (b) SDANN and (c) RMSSD

Figure 5. Overall classification

In Table V, a set of three exams previously classified

as control, i.e., pathological or suspicious. For these

exams, at least two parameters are in the suspicious

classification based on the proposed (µ ± σ) analysis. The

third exam, for example, has STV, RMSSD and LF/HF

out of the proposed normality interval.

All the three exams classified as abnormal according to

the FHRV analysis and also were classified as

pathological by the conventional CTG analysis:

ctg20011218_2348371; ctg20001213_0948395 and

ctg20000709_043356.

TABLE IV. STATISTICAL AND FREQUENCY DOMAIN PARAMETERS FOR

NORMAL EXAMINATIONS

Exam STV HF

(n.u.) LF/HF SDNN RMSSD

ctg20000304-

0409053 0.81 13.72 6.28 21.68 2.95

ctg20000209-

0834583 0.90 10.62 8.41 26.69 2.81

ctg20000202-

0408315 0.98 11.94 7.37 31.86 3.41

ctg20000228-

1258193 1.09 10.87 8.19 30.73 3.64

ctg20000329-

0541413 0.85 13.15 6.60 22.57 2.80

TABLE V. STATISTICAL AND FREQUENCY DOMAIN PARAMETERS FOR

SUSPICIOUS OR PATHOLOGICAL EXAMINATIONS

Exam STV HF

(n.u.) LF/HF SDNN RMSSD

ctg20000729-2151501 1.22 13.31 3.94 28.40 3.42

ctg20011204-0845235 0.78 9.51 9.50 21.72 2.18

ctg20000630_0916173 0.66 8.49 5.74 60.55 2.09

Nevertheless, the FHRV the results presents that those

parameters must not be used as the unique analysis of the

fetal state. Other exams show a divergence if you

compare the two classification system. For example, the

exams ctg20000521-1402455, ctg20000203-1942093 and

ctg20000518-2034363 belongs to the proposed normality

classification but were previously classified as suspicious

or pathological. In all these cases, if the FHRV was

considered alone, fetal health problems could not be

detected.

On the other hand, the FHRV analysis may expands

the conventional analysis. The ctg20010223-1429403

exam is previously classified as normal in the

conventional analysis. Although, this exam presents the

lowest short term variability, STV=0.61, and the lowest

RMSSD, 1.93. The LTV=10.69 and HF=8.49 are

considered low values. This may indicate low variability

and a very small contribution of the high frequency

components, related to the parasympathetic system.

According to this analysis, the exam could be classified

as suspicious or pathological.

Another example is the ctg20010626-2358115 exam,

also classified as normal before. It presents the highest

SDNN value, 81.18 and high values for the HF, 22.09

14%

35% 21%

30%

Overall Classification

Abnormal

Suspicious

Attention

Normal



187

and the RMSSD, 4.70. This may indicate a suspicious

fetal health status, with a strong influence of the

parasympathetic system over the desired balance.

IV. CONCLUSIONS

The interval between heart beats can be considered an

important parameter for the detection of the fetal health

status.

The FHRV analysis based on the CTG examination is

a viable approach for the obstetric practice, since the

visual analysis is very subjective. For computerized CTG

systems this analysis can be applied as a second level and

complimentary detector of fetal distress.

The results presented require that the FHRV analysis

must not be considered as the only monitored parameters.

The conventional analysis is strictly necessary.

Future works could consider other statistical indices,

such as percentiles or quartiles for the classification

criteria and also other statistical and geometrical

measures can be considered, to improve the proposed

analysis.

ACKNOWLEDGMENT

The authors thank the Trium Analysis Online GmBH

in Munich, CNPq (Brazil), Funcap/FINEP (Brazil) for

funding PAPPE Project and the Centre of Bioengineering

at University of Leicester, Leicester, UK.

REFERENCES

[1] Task Force of the European Society of Cardiology and the North

American Society of Pacing and Electrophysiology, Heart rate

variability-standards of measurement, physiological interpretation,

and clinical use, European Heart Jounal, vol. 17, pp. 354-381,

1996.

[2] M. Signorini, G. Magenes, S. Cerutti, and D. Arduini, “Linear and nonlinear parameters for the analysis of fetal heart rate signal from

cardiotocographic recordings,” IEEE Transactions on Biomedical Engineering, vol. 50, pp. 365-374, 2003.

[3] I. Ingemarsson, E. Ingemarsson, Spencer, A. D. John, Fetal Heart

Rate Monitoring–A Practical Guide, New York: Oxford Medical Publications, Oxford University Press, 1993.

[4] J. A. L. Marques, P. C. Cortez, and J. P. do V. Madeiro, “Detecção

de alterações da frequência cardíaca fetal e do tônus uterino materno em exames cardiotocográficos utilizando transformada de

hilbert. porto de galinhas,” Brazilian Conference in Biomedical

Engineering, 2008. [5] D. N. Lebrun, Analysis of Neonatal Heart Rate Variability and

Cardiac Orienting Responses, Dissertação (Mestrado)-University

of Florida, 2003. [6] O. Sibony, J. Fouillot, M. Benaoudia, A. Benhalla, J. Oury, C.

Sureau, and P. Blot, “Quantification of fetal heart rate variability

by spectral analysis of fetal well-being and fetal distress,” European Journal of Obstretics and Gynecology and Reproductive

Biology, vol. 54, pp. 103-108, 1994.

[7] M. Ferrario, M. Signorini, G. Magenes, and S. Cerutti, “Comparison of entropy-based regularity estimators: Application

to the fetal heart rate signal for the identification of fetal distress,”

IEEE Transactions on Biomedical Engineering, vol. 53, pp. 119-125, 2006.

[8] M. Malik and A. J. Camm, “Components of the heart rate

variability-What they really mean and what we really measure,” American Journal of Cardiology, vol. 72, pp. 821-822, 1993.

[9] Mathworks. Matlab. Nov. (2011). [Online]. Available:

http://www.mathworks.com

[10] J. P. do V. Madeiro, “Sistema automático para análise de

variabilidade da freqüência cardíaca,” Master s Thesis, Federal

University Federal of Ceará, 2007.

J. A. L. Marques was born in Fortaleza-CE, Brazil,

in 1973. Dr. Marques graduated in Electrical

Engineering at the Federal University of Ceará (UFC) in 1996. He then concluded his Master’s

studies at UFC in 2006 and his doctoral studies at

UFC in 2010 (with an internship at Trium Analysis Online GmBH, in Munich, Germany - 2007). He

concluded postdoctoral studies at the University of

Leicester, UK in 2012. He is currently with the Department of Informatics, University Lusíada of Angola, in Lobito, Angola, where he

heads the research group of Health Informatics and is the Director of the

Research, Studies and Post-Graduation Center. His current research is focused on biological time series analysis (such as heart, brain and

others) using digital signal processing techniques based on linear and

nonlinear approaches. He is also heading a research project: the “NeuroSapiens Project”, a joint research with the University of Cape

Town, South Africa, a biological signal monitoring and analysis system

for cognitive and affective neuroscience studies in Angola with post-civil war students and families.

P. C. Cortez was graduated in Electrical

Engineering at the Federal University of Ceará

(UFC) in 1982. He then concluded his Master’s and Doctoral studies at UFC in 1992 and 1996

respectivelly at Federal University of Paraiba –

Campina Grande. His is an Associate Professor Level III from the Department of Teleinformatics

Engineering at UFC. His research is focused on

Artificial Vision, primarily working with 2-D and 3-D contours poligonal modeling, pattern recognition, digital imaging

segmentation digital signal processing, biomedical images, computer-

aided intelligent systems for biomedical signal analysis, telemedicine applications and embedded systems.

J. P. do V. Madeiro was graduated in Electrical Engineering at the Federal University of Ceará

(UFC) in 2006. He then concluded his Master’s

and Doctoral studies at UFC in 2007 and 2013 also at the Federal University of Ceara – Department of

Teleinformatics Engineering. He also worked at

the University of Leicester during his Doctoral studies workins with electrograms during

persistent atrial fibrilation. He works at Ministério Público Federal and

his research is focused on digital signal processing, computer-aided diagnostic systems, automatic ECG parameter extraction, electrograms

and the application of nonlinear techniques for cardiologic signals.

Fernando S. Schlindwein was born in Porto Alegre, Brazil, in 1956. He graduated with a First

Class Honours degree as an Electronic Engineer in

1979 from the Federal University of Rio Grande do Sul, Brazil, with an extension degree in

Nuclear Engineering. After a short time in

industry (Aços Finos Piratini, a steel mill) he obtained an MSc in Biomedical Engineering from

the Coordination of Post-Graduation Programmes in Engineering of the

Federal University of Rio de Janeiro (COPPE/UFRJ), Brazil in 1982, a PhD in Biomedical Engineering from the Department of Surgery of the

University of Leicester, England in 1990, and a DSc in Biomedical

Engineering from the Federal University of Rio de Janeiro (UFRJ) in 1992. He was a Senior Lecturer associated with UFRJ from August

1980 until 1992, when he joined the Department of Engineering at the

University of Leicester where he is a Reader in Bioengineering. He did his military service at Colégio Militar of Porto Alegre where he was

First Cadet in Infantry. He has also been a Senior Lecturer of the



188

Department of Electronics of the Brazilian Navy Academy for Officers

in Rio de Janeiro, Brazil in the early 1980s. His current research

interests are real-time digital signal processing, with more intense

research activities in i) cardiac arrhythmias, especially atrial fibrillation;

ii) heart rate variability and automatic arrhythmia monitoring using the

ECG, and iii) microprocessor-, microcomputer- and Digital Signal

Processor-based systems.



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