ORIGINAL ARTICLE

Feature selection using swarm-based relative reduct techniquefor fetal heart rate

H. Hannah Inbarani • P. K. Nizar Banu •

Ahmad Taher Azar

Received: 26 July 2013 / Accepted: 17 January 2014

� Springer-Verlag London 2014

Abstract Fetal heart rate helps in diagnosing the well-

being and also the distress of fetal. Cardiotocograph (CTG)

monitors the fetal heart activity to estimate the fetal tach-

ogram based on the evaluation of ultrasound pulses

reflected from the fetal heart. It consists in a simultaneous

recording and analysis of fetal heart rate signal, uterine

contraction activity and fetal movements. Generally CTG

comprises more number of features. Feature selection also

called as attribute selection is a process of selecting a

subset of highly relevant features which is responsible for

future analysis. In general, medical datasets require more

number of features to predict an activity. This paper aims at

identifying the relevant and ignores the redundant features,

consequently reducing the number of features to assess the

fetal heart rate. The features are selected by using unsu-

pervised particle swarm optimization (PSO)-based relative

reduct (US-PSO-RR) and compared with unsupervised

relative reduct and principal component analysis. The

proposed method is then tested by applying various clas-

sification algorithms such as single decision tree, multi-

layer perceptron neural network, probabilistic neural

network and random forest for maximum number of classes

and clustering accuracies like root mean square error, mean

absolute error, Davies–Bouldin index and Xie–Beni index

for minimum number of classes. Empirical results show

that the US-PSO-RR feature selection technique outper-

forms the existing methods by producing sensitivity of

72.72 %, specificity of 97.66 %, F-measure of 74.19 %

which is remarkable, and clustering results demonstrate

error rate produced by US-PSO-RR is less as well.

Keywords Unsupervised � PSO � Feature selection �Relative reduct � Fetal heart rate � Cardiotocogram

1 Introduction

Classically, a graphical representation of fetal heart rate

(FHR) signal is visually inspected by clinician, whose task

is to identify and to classify the signal patterns. The

interpretation of heart rate patterns obtained by fetal

monitoring relies mainly on the definition of the basal level

of the FHR signal and its variability. The basal level of

FHR signal, called the baseline, is considered as the run-

ning average heart rate in the absence of external stimuli

during periods of fetal rest. The FHR variability is defined

in the aspect of its transient increase (acceleration pattern)

or decrease (deceleration pattern). Accelerations are the

result of fetal movements and to identify the fetal well-

being, while decelerations are the symptoms of fetal dis-

tress usually indicating the risk of fetal hypoxia.

During the crucial period of labor, FHR monitoring is

used as the main screening test of the fetal acid-base bal-

ance [1]. Visual analysis of FHR recording does not

guarantee a correct assessment of the fetal state, and the

accuracy of the interpretation depends on clinician’s

experience. It was concluded in [2], that the weakness of

H. Hannah Inbarani

Department of Computer Science, Periyar University,

Salem, India

e-mail: hhinba@gmail.com

P. K. Nizar Banu

Department of Computer Applications,

B.S. Abdur Rahman University, Chennai, India

e-mail: nizarbanu@gmail.com

A. T. Azar (&)

Faculty of Computers and Information, Benha University,

Benha, Egypt

e-mail: ahmad_t_azar@ieee.org; ahmad.azar@fci.bu.edu.eg

123

Neural Comput & Applic

DOI 10.1007/s00521-014-1552-x

cardiotocograph (CTG) still lies in a generally poor stan-

dard of interpretation, and the contribution of the human

factor demonstrated by high intra- and inter-observer var-

iability. To decrease the subjective nature of fetal state

evaluation, computerized decision support systems should

be developed for supporting the process of medical diag-

nosis [2]. The earliest work for automatic analysis was

completely based on clinical guidelines for CTG assess-

ment [3]. Recently, methods derived from adults HRV

research were similarly used for FHR analysis [4]. The

statistical description of CTG tracings was employed in [4,

5]. Another features derived from wavelets transform were

used in [6, 7]. A combination of neural networks and fuzzy

models, namely neuro-fuzzy systems were also employed

for the classification of fetal cardiotocograms. A fuzzy

inference system based on artificial neural network

(ANBLIR) with epsilon-insensitive learning [8] was used

for the prediction of fetal outcome on the basis of FHR

signal analysis [9, 10]. The epsilon-insensitive learning

employing the principles of the statistical learning theory

[11, 12] resulted in high prediction accuracy.

Proper heart activity is an indicator of adequate fetal

blood oxygenation and shows that the central nervous

system is intact and provides a good modulating control.

Hence, the analysis of FHR is an essential element of

diagnostic process for the assessment of fetal status and

well-being during pregnancy and labor. Original features of

FHR are derived from three domains: the time domain, the

frequency domain and the morphological domain. Mor-

phological domain utilizes medical definitions of morpho-

logical features, which have already been used with

antepartum [13, 14] and intrapartum case [14, 15]. As the

number of features increases, time taken to predict the

status of fetal in critical conditions during pregnancy is a

challenging task. In some cases, essential features like

baseline value of FHR, number of accelerations and fetal

movements per second are left without considering for

further analysis. These kinds of features may also give rise

for predicting different classes which has been already

recorded. In certain cases, values of features may be similar

but they differ in classes they belong to. Here comes the

need to apply rough sets [16]; a mathematical tool to dis-

cover the dependencies among the features. When rough

set is combined with swarm intelligence, significant fea-

tures are identified in a lesser time, since this technique is

inspired from the nature. The objective of this paper is to

identify the vital features that help in assessing the fetal

heart rate and to diagnose the status of fetus.

Rough set can be used as a tool to discover data

dependencies and to reduce the number of features con-

tained in a dataset using the data alone [16]. Feature

selection algorithms are classified as filter approach and

wrapper approach. Filter-based methods does not depend

on any induction algorithm, and therefore, these methods

are efficient than wrapper-based methods. Although

wrappers produces good results, they are expensive to run

and will reduct more number of features. PSO is a filter-

based heuristic method that guides in feature selection by

providing optimal minimal subset every time. PSO-based

algorithms tend to select the features in all possible ways,

rather than sticking to exactly minimum set of features

[17]. In this paper, unsupervised swarm-based relative re-

duct technique, hybrid of both swarm intelligence and

rough set, is applied to identify the most essential features,

and its accuracies are tested with both clustering and

classification methods that leads in early diagnosis. On

using this hybrid technique, the strict requirement of fitness

function is relaxed and the features are selected by intro-

ducing the dependency among the attributes. This proves to

be a flexible approach in practical applications.

Most of existing algorithms for fetal heart rate focus

only on identifying the status of fetus with the help of

classification techniques. We insist classification alone is

not important; features that have highest impact for clas-

sification is also equally important. This research work

provides the purpose of finding the useful features in

unsupervised approach and the empirical results presented

in this paper reveals it.

The rest of this paper is organized as follows. Section 2

surveys related work. Section 3 discusses rough sets along

with the details of rough sets and fetal heart rate. Feature

selection using rough sets including the algorithm and its

working procedure is given in Sect. 4. Section 5 presents

the experimental results with the performance analysis, and

this paper is concluded in Sect. 6.

2 Related work

Fetal heart rate (FHR) monitoring is mainly used to find out

the amount of oxygen a fetus is acquiring during the time

of labor [18]. In the research carried out so far, it is

observed that 50 % of the death and long-term disablement

occurs due to abnormal FHR pattern; even if it is recog-

nized in the earlier stage, it is not communicated properly

due to lack of knowledge and seriousness, and it is left

without taking appropriate action. Though numerous

measures are taken for the FHR, features that are highly

informative should be identified and recommended to the

physicians for further analysis. This paper aims to identify

the important features by preserving the details of the

dataset.

Features derived from wavelets transform were used in

[6, 19, 20]. A system identification approach to estimate

parameters from FHR and uterine pressures was described

in [21]. The extraction of nonlinear features for FHR

Neural Comput & Applic

123

analysis was applied in different studies. The measure of

fractal dimension was performed by Chaffin [22], Gough

[23] and Felgueiras [24]. Another attempt was to measure

the length of FHR curve using the Higuchi’s method [25].

In this study, two distinct fractal structures within the FHR

variation were identified and that fractal features of heart

rate of healthy normal fetuses changed significantly during

pregnancy period. A hybrid approach for FHR classifica-

tion utilizing grammatical evolution for features construc-

tion was also presented in [26], and the newly constructed

features were tested using a neural network, which was

trained based on a hybrid method involving a combination

of a genetic algorithm and a local optimizer.

A review of the papers that analyzed the spectrum of

FHR either antepartum or intrapartum was given in [27]. In

[28], the spectral power of fetal heart rate variability was

analyzed in relation to fetal scalp blood pH. The absolute

and normalized spectral power in the low-frequency band

(0.04–0.15 Hz) and in the high-frequency band

(0.4–1.5 Hz) was measured. It was found that normalized

low-frequency and normalized high-frequency power of

fetal heart rate variability is associated with fetal scalp

blood pH.

Recently, a system identification approach to estimate

parameters from FHR and uterine pressures was described

in [21]. The extraction of nonlinear features for FHR

analysis was applied in different studies. The measure of

fractal dimension was performed by [22–24]. A review of

the different estimations of fractal dimension was given in

[29]. Another nonlinear methods as the approximate

entropy (ApEn) and sample entropy (SampEn) were used

and proved their applicability in FHR analysis [30–32].

One more method for nonlinear analysis is Lempel–Ziv

complexity used in [33]. The conventional features were

evaluated and compared to the nonlinear ones for intra-

partum FHR classification in [34]. It was proven that the

addition of nonlinear features improved accuracy of clas-

sification. The best classification results were achieved

using a combination of conventional and nonlinear features

with sensitivity of 73.4 %, specificity of 76.3 % and

F-measure of 71.9 %. The best selected nonlinear features

were: Lempel–Ziv complexity, sample entropy and fractal

dimension estimated by Higuchi’s method. In this paper,

we have used morphological features of FHR, and the

unsupervised PSO-based relative reduct (US-PSO-RR)

approach produces sensitivity of 72.72 %, specificity of

97.66 % and F-measure of 74.19 %.

The use of rough set theory to construct reducts in a

supervised way for reducing the number of features in an

unsupervised clustering is given in [35]. The authors of

[36] proposed a novel heuristic algorithm based on rough

set theory to find out the feature subset. An application of

rough set method for feature selection in pattern

recognition is presented in [37]. A number of feature

selection methods for decision system based on the rough

set theory approach are reviewed and given in [38]. A

novel approach for fetal heart rate classification is given in

[26]. In this paper, rough-set-based approach is used to find

the features that can be used in future for diagnosis.

In [39], the baseline’s fluctuations were investigated

along gestation, in relation to fractal and nonlinear prop-

erties. The fractal properties were evaluated by applying

detrended fluctuation analysis. The nonlinear properties

and time ordering were explored by applying the scaling

magnitude and sign analyses. The main findings were that

the baseline showed fractal and particular nonlinear anti-

correlated fluctuations which suggests that the baseline

may provide on information concerning the functional

integration of cardiac regulatory mechanisms.

For classification of the FHR signals, many different

approaches were proposed in the literature. Among these

are the methods based on fuzzy set theory and fuzzy logic.

A fuzzy system was developed for the assessment of the

fetal state in [40, 41]. The developed system represents the

fuzzy extension of an existing classical (crisp) expert sys-

tem [42] assessing features of five-min segments of FHR

recording. A higher performance of the fuzzy system in

comparison with the crisp expert system was demonstrated

as measured by a degree of agreement with clinical experts.

A set of classifiers of the FHR signals based on fuzzy

inference systems was presented in [43, 44]. The main goal

was to identify the intrauterine growth retardation and

diabetes type I based on gestational age and quantitative

description of the signal in time and frequency domains. In

[45], the signal describing parameters were used as inputs

to fetal distress fuzzy monitoring system.

Another group of methods for the fetal state assessment

constitutes procedures using learning and generalization

capabilities of artificial neural networks (ANN). In [46], a

five-layer feedforward neural network was constructed for

the fetal state assessment. The classification accuracy was

calculated in relation to the clinical expert’s interpretation.

An application of self-organizing neural networks was

done in [47]. A comparison of the classification perfor-

mance when using both types of networks was presented in

[48]. The obtained results showed a higher classification

accuracy of the multilayer perceptron network. The ANN

classifiers were compared to classical statistical methods

using quantitative description of FHR signals in [49]. The

ANN resulted in an improved quality of the FHR record-

ings assessment. It was also identified that the multilayer

perceptron networks are the most suitable for evaluating

the fetal state on the basis of quantitative analysis of FHR

recordings. These findings were also emphasized in [50].

Some practical issues on ANN application to fetal state

assessment on the basis of quantitative description of FHR

Neural Comput & Applic

123

signals were presented in [51, 52]. A comparison of the

fetal outcome prediction capabilities using a three-layer

ANN and fuzzy clustering algorithm was demonstrated in

[53]. Application of ANN showed a higher evaluation

quality; however, a fuzzy approach was suggested for

databases of smaller size. A hybrid approach for FHR

classification utilizing grammatical evolution for features

construction was also presented in [26]. The newly con-

structed features were tested using a neural network, which

was trained based on a hybrid method involving a combi-

nation of a genetic algorithm and a local optimizer. The

performance of conventional classifiers such as k-nearest

neighbors (k-NN), linear discriminant classifier (LDC) and

quadratic discriminant classifier (QDC) was compared to

the hybrid approach method. The hybrid approach method

overwhelmed the approach with the conventional classifi-

ers and the principal component analysis (PCA) stage. The

obtained results on real dataset demonstrated an overall

specificity and sensitivity of 90 %. A simple analysis of

US-PSO-RR approach is given in [54].

From the study, it is understood that most of the papers

focus on classifying the FHR; we comprehend looking for

informative features is also desirable in near future. If

classification alone gets more attention; when the dataset

grows with huge number of samples and features concur-

rently, time it requires to classify and diagnose is unpre-

dictable. In this modern era, physicians track for specific

constraint that is complex during labor and attempt to

reduce the risk as much as possible. Applying rough sets

alone for this kind of problem is not sufficient. Intelli-

gence-based methodology will produce remarkable results.

This motivated us to use US-PSO-RR approach, a combi-

nation of rough sets and swarm intelligence, which out-

performs other conventional methods used in the literature

by producing overall specificity of 97.66 %.

3 Rough set: preliminaries

Rough set theory [16] is an extension of conventional set

theory that supports approximations in decision making. It

possesses many features in common with Dempster–

Shafer theory of evidence. The rough set is the approxi-

mation of a vague set by a pair of precise concepts called

lower and upper approximations. Lower approximation is

a description of the domain objects which definitely

belong to the subset of interest, whereas the upper

approximation is a description of the objects that may or

may not belong to the subset. Rough sets are applied in

many domains such as medicine, finance, telecommuni-

cation, vibration analysis, conflict resolution, intelligent

agents, image analysis, pattern recognition, control theory,

process industry and marketing [55]. Feature selection

aims to choose a subset of original features which pro-

vides the most useful information by preserving the sig-

nificant details present in a given dataset. This can be

achieved by detecting and ignoring the noisy features

from the dataset.

Rough sets, introduced by Pawlak [16], helps to spot

the most informative features by considering the condi-

tional attributes alone without requiring any additional

information and helps in knowledge discovery. Rough set

approach is an approximation of sets resulting in the form

of granular computing and thus becomes a part of com-

putational intelligence. Rough set approach forces to dis-

cover to what extent a given set of objects approximates

another set of objects of interest [56]. The main advantage

of rough set analysis is it requires no additional parame-

ters to operate other than the available data. When FHR is

considered, it is not possible for the physicians to say

whether two or more feature values are similar and to

what extent they are same, they may be alike or differ due

to noise leading to improper or late diagnosis. Most of the

existing feature selection algorithms are supervised; it

tends to depend on the decision attribute. For medical

diagnosis, the physicians require lot of experiments to

predict some sort of disorders. In such situation, unsu-

pervised feature selection algorithm plays a vital part to

assist physicians. Rough sets helps in removing the irrel-

evant attributes without any information loss. The fol-

lowing sections explain how rough set is applied for fetal

heart rate with appropriate notations.

3.1 Rough sets and fetal heart rate

Fetal heart rate dataset can also be called as information

(Table 1) according to rough sets; it is represented as a

table shown in Table 1, and each row represents a case and

column represents a feature. Information table comprises a

set of cases (rows) called universe (U) and a set of features

(columns) represented as ‘A.’ Hence, an information table

is a pair I = (U, A), where U is a non-empty finite set of

cases called the universe and A is a non-empty finite set of

features such that a: U ! Va for every a [ A. The set Va is

called the value set of a. Fetal heart rate dataset comprises

21 features and 10 classes. Feature selection technique is

applied to identify the most informative features to diag-

nose quickly.

3.2 Lower and upper approximations

Information system which contains all the 21 features with

2,126 samples is denoted as I = (U, A). U is the universe

with non-empty set of finite samples; A is a non-empty

finite set of conditional attributes. A decision system

includes conditional attributes and one or more decision

Neural Comput & Applic

123

attributes. Fetal heart dataset discussed in this paper has

one decision attribute with 10 different classes. Each row

in decision system holds recorded values of every cases

corresponding to every feature.

3.3 Lower approximation

Lower approximation refers to the set of cases that can

definitely belong to a given class. Let X � U, the F-lower

approximation FX of a set X can be defined as

FX ¼ f x 2 Uj x½ �f� Xg ð1Þ

3.4 Upper approximation

Upper approximation refers to the set of cases that possibly

belongs to a given class. Let X � U, the F-upper approx-

imation �FX of a set X can be defined as

�FX ¼ fx 2 Uj½x�f \ X 6¼ ; ð2Þ

3.5 Equivalence relations

A decision system may contain same or indiscernible

objects several times, or some of the attributes may be

surplus which increases the size of the dataset unneces-

sarily. Let I ¼ U;Að Þ be an information system, then with

any B � A there is associated an equivalence relation

INDAðBÞ.INDA Bð Þ ¼ x; x0ð Þ 2 U2j8a 2 Ba xð Þ ¼ a x0ð Þ

� �ð3Þ

INDA Bð Þ is called the B-indiscernibility relation. If

x; x0ð Þ 2 INDA Bð Þ; then objects x and x0 are indiscernible

from each other by attributes from B. The equivalence

classes of the B-indiscernibility relation are denoted as x½ �B.

Positive region of the partition U=Q with respect to P is

the set of all elements of U that can be uniquely classified

as blocks of the partition; U=Q by means of P can be

defined as

POSP Qð Þ ¼ [X2UjQ

FX ð4Þ

3.6 Relative dependency measure and fitness value

The unsupervised relative dependency measure for a par-

ticular particle is defined as follows:

cR að Þ ¼ U = INDðRÞj jU = INDðRÞ [ fagj j 8 a 62 R ð5Þ

where R is the subset selected by the particle, and the mean

dependency of selected feature subset on all the features

that are not selected by the particle is used as the fitness

value of the particle Xi.

Fitness Xið Þ ¼ cR yð Þ8y 62 R ð6ÞTa

ble

1O

rig

inal

set

of

feat

ure

sin

feta

lh

eart

rate

dat

aset

LB

AC

FM

UC

DL

DS

DP

AS

TV

MS

TV

AL

TV

ML

TV

Wid

thM

inM

axN

max

Nze

ros

Mo

de

Mea

nM

edia

nV

aria

nce

Ten

den

cy

12

00

.00

00

.00

00

.00

00

.00

00

.00

00

.00

07

30

.54

32

.46

46

21

26

20

12

01

37

12

17

31

13

20

.00

60

.00

00

.00

60

.00

30

.00

00

.00

01

72

.10

10

.41

30

68

19

86

11

41

13

61

40

12

0

13

30

.00

30

.00

00

.00

80

.00

30

.00

00

.00

01

62

.10

13

.41

30

68

19

85

11

41

13

51

38

13

0

13

40

.00

30

.00

00

.00

80

.00

30

.00

00

.00

01

62

.40

23

11

75

31

70

11

01

37

13

41

37

13

1

13

20

.00

70

.00

00

.00

80

.00

00

.00

00

.00

01

62

.40

19

.91

17

53

17

09

01

37

13

61

38

11

1

Neural Comput & Applic

123

4 Feature selection and rough sets

An information system or a decision system may have

more than one reducts. Any one subset can be used to

replace the original table. Finding all the reducts from a

decision system or an information system is NP-hard [57].

In most of the real applications, it is usually not necessary

to find all of them. It is sufficient to compute only one

reduct [58].

When there occurs more than one set of reducts, one

possibility of selecting the reduct is to consider the subset

with the least number of features. This selection process is

not suitable for all applications. At the time of applying

these kinds of feature selection techniques, classification

accuracy measures can be applied and the reduct set which

gives the highest classification accuracy shall be taken.

Conditional attributes have been taken from the fetal

heart rate dataset, and therefore, unsupervised feature

selection technique called US-PSO-RR proposed in [59] is

applied.

4.1 Unsupervised PSO-based relative reduct

(US-PSO-RR)

Particle swarm optimization algorithm, one of the swarm

intelligence approach is based on the principles of collec-

tive behavior of swarms. Relative reduct algorithm a

rough-set-based technique includes all the attributes at

once and then checks for the dependency between the

conditional attributes and decision attribute. Unsupervised

feature selection algorithm and unsupervised relative re-

duct (USRR) [60] calculate the dependency measures for

every attribute without considering decision attribute. If the

dependency between the conditional attribute is 1, then the

subset should be retained.

Rough-set-based feature reduction algorithms like

USRR [60] and unsupervised quick reduct (USQR) [61]

can also be applied, but it will take more time to converge

as proved in [59]. When PSO is combined with rough set,

rather than taking all combinations, it starts by representing

every particle’s position as a binary bit in which the attri-

butes marked as 1’s are taken for further processing. Each

particle’s position is considered as an attribute subset.

With the intelligence of swarm, features are covered in all

combinations; with the influence of rough sets, features that

are not highly dependent are removed; rest of the features is

retained. This is how swarm-based relative reduct is focused

in this paper, and the same is detailed in next section.

For example, if five features from FHR dataset, namely

base line value (LB), number of accelerations per second

(AC), number of fetal movements per second (FM), num-

ber of uterine contractions per second (UC) and number of

light decelerations per second (DL) are taken and if the

selected particle is (0, 1, 0, 1, 1), then the feature subset is

AC, UC and DL. The technique applied in this paper is

discussed below, and the algorithm is given in Fig. 1 and

described in Sect. 4.2.

4.2 Working procedure of US-PSO-RR algorithm

The unsupervised PSO-RR algorithm calculates a reduct

set without generating all possible subsets as a common

relative reduct approach. It starts by selecting random

values for each particle and velocity. First, a population

of particles is constructed. For each particle Xi, 1’s are

taken as the selected features and 0’s are considered as

removed features. Average dependency of every selected

feature on every non-selected feature is computed. The

feature subset of particle is taken as the reduct set, if the

mean dependency equals 1. The highest relative depen-

dency value (pbest) of each particle is retained, and the

best value of the entire population is retained as the

global best value if the mean dependency value is not

equal to 1.

The overall process of US-PSO-RR feature selection

can be seen in Fig. 2. Initially, swarms are constructed

with random positions and velocities. For each particle,

fitness function is evaluated. If the fitness evaluation is

better than previous best, then this particle becomes the

current best, and its position and fitness are stored. This

position represents the best feature subset on computing

the dependency among the attributes. Next, the current

particle’s fitness is compared with the population’s

overall previous best fitness. If the current value is better

than the values computed so far, then this is set to the

current particle’s position. This position represents the

best feature subset encountered so far and dependency

among the attributes is computed and it is stored as a

reduct set. The velocity and position of a particle is then

updated. This process is carried out until good fitness is

found.

For selecting features, PSO is initialized with a popu-

lation of particles. Each particle is treated as a point in an

S-dimensional space. The ith particle is represented as

Xi = (x1, x2, …, xis). Pi = (pi1, pi2, …, pis) is the pbest,

best position, of any particle, and gbest is the global best

particle. The velocity of a particle is Vi = (vi1, vi2, … vis).

The position and velocity are updated as

Vid ¼ w* vid þ c1 � randðÞ � pid � xidð Þ þ c2 � randðÞ� pgd � xid

� �ð7Þ

Here, w is the inertia weight, and c1 and c2 are accel-

eration constants. Based on velocity, particle’s position is

updated as follows:

Neural Comput & Applic

123

• If V B xg, randomly change V bits of the particle,

which are different from that of gbest.

• If V [ xg, change all the different bits to be the same as

that of gbest and a further (V-xg) bits should be

changed randomly.

Where w can be calculated as follows

w ¼ wmax �wmax � wmin

itermax

iter ð8Þ

Here, wmax is the initial value of the weighting coeffi-

cient, wmin is the final value, itermax is the maximum

number of iterations and iter is the current iteration as

given in [13].

5 Experimental results and discussion

Fetal heart rate dataset is available in UCI machine

learning repository for analysis [62]. It can be obtained

from http://archive.ics.uci.edu/ml/datasets/Cardiotoco

graphy. This dataset consists of 2,126 samples with 21

features with 10 different classes. This dataset is classified

and labeled by three expert obstetricians from Portugal in

two different forms; one as 10 classes with respect to

morphologic pattern and the other one is 3 classes based on

the fetal state such as normal, suspect and pathologic. We

have used our approach for 10 class classification. Out of

21 features, 13 features are considered as highly

Fig. 1 US-PSO-RR algorithm

Neural Comput & Applic

123

informative by US-PSO-RR method, and the accuracy

measures also gives highest accuracy rate for these 13

selected features. All the 21 features are listed in Table 1,

with a set of sample values. The reduct set is given in

Table 2. It is suggested that in future these 13 features

alone can be taken for further analysis by the physicians.

Measures of diagnostic accuracy are used to assess the

quality of reduct set generated by the US-PSO-RR algo-

rithm, and it is compared with USRR and PCA [63]. The

diagnostic measures are used to determine the presence or

absence of a disease. Objective measures like sensitivity,

specificity, positive predictive value (PPV) and negative

predictive value (NPV) are used for validation.

5.1 Performance analysis

Performance of the proposed method for fetal heart rate

was evaluated by using performance indices such as

sensitivity, specificity, PPV, NPV, accuracy and F-mea-

sure. Sensitivity is also called recall rate in some fields and

measures the proportion of actual positives which are

correctly identified, while specificity measures the pro-

portion of negatives which are correctly identified. The

positive predictive value or precision rate is the proportion

of positive test results that are true positives (such as cor-

rect diagnoses). It is a critical measure of the performance

of a diagnostic method, as it reflects the probability that a

positive test reflects the underlying condition being tested

for. Its value does, however, depend on the prevalence of

the outcome of interest, which may be unknown for a

particular target population. The NPV is a summary sta-

tistic used to describe the performance of a diagnostic

testing procedure. It is defined as the proportion of subjects

with a negative test result who are correctly diagnosed. A

high NPV for a given test means that when the test yields a

negative result, it is most likely correct in its assessment. In

the familiar context of medical testing, a high NPV means

that the test only rarely misclassifies a sick person as being

healthy. Some of the main formulations are defined as

follows:

5.1.1 Classification accuracy

The classification accuracy is a common method that is

used in the pattern recognition applications. The classifi-

cation accuracy for the experiment is taken as the ratio of

the number of samples correctly classified to the total

number of samples.

Accuracy ¼ TP þ TN

TP + FP + FN + TN� 100 % ð9Þ

5.1.2 Sensitivity and specificity

Sensitivity and specificity are statistical measures of the

performance of a classification test. Sensitivity measures

the proportion of actual positives which are correctly

Begin Generate Swarm with randomposition and random velocity

Evaluate Fitness

Gather SelectedSubsets

Evaluate dependency amongattributes Return Best Subset

Choose next feature

Update Velocity and position

Continue

Compute Dependency

Stop

Continue

Stop

Fig. 2 Block diagram for US-PSO-RR

Table 2 Features selected by US-PSO-RR algorithm

S.

no

Name of the

feature

Description

1 LB Baseline value of FHR in terms of beats per

minute

2 AC Number of accelerations per second

3 FM Number of fetal movements per second

4 UC Number of uterine contractions per second

5 DL Number of light decelerations per second

6 MSTV Mean value of short-term variability

7 ALTV Percentage of time with abnormal long-term

variability

8 MLTV Mean value of long-term variability

9 Width Width of fetal heart rate histogram

10 Min Low frequency of the histogram

11 Nmax Number of histogram peaks

12 Median Median of FHR histogram

13 Tendency Tendency of FHR histogram

Neural Comput & Applic

123

identified as such (e.g., the percentage of sick people who

are correctly identified as having the condition). Specificity

measures the proportion of negatives which are correctly

identified (e.g., the percentage of healthy people who are

correctly identified as not having the condition). For sen-

sitivity and specificity analysis, we use the following

expressions:

Senstivity ¼ TP

TP + FN� 100 % ð10Þ

Specificity ¼ TN

FP + TN� 100 % ð11Þ

5.1.3 Positive predictive value (PPV), negative predictive

value (NPV)

Positive predictive value is the proportion of positive test

results that are true positives (such as correct diagnoses). It

is a critical measure of the performance of a diagnostic

method, as it reflects the probability that a positive test

reflects the underlying condition being tested for. The NPV

is the probability that gives a negative result, when the fetal

heart rate measure is absent. It is defined as the proportion

of subjects with a negative test result who are correctly

diagnosed. A high NPV for a given test means that when

the test yields a negative result, it is most likely correct in

its assessment.

PPV ¼ TP

TP + FP� 100 % ð12Þ

NPV ¼ TN

TN + FN� 100 % ð13Þ

5.1.4 F-measure

The F-measure or F-score can be used as a single measure

of performance of the test. The F-score is the harmonic

mean of precision and recall and can be calculated as:

F�measure ¼ 2 precision� recallð Þprecisionþ recall

ð14Þ

As shown in Table 3 and Fig. 3, the F-measure for

features selected using US-PSO-RR method is higher than

the other two algorithms. Table 3 shows the values of

diagnostic measures of the reduct set. The diagnostic

measures are applied for 13 features selected by the pro-

posed approach and 10 classes. Average of every measure

is also obtained to analyze the significance of selected

features, and it is observed that it gives 70 % and above

which is acceptable for real applications. These are all the

classification measures, so classification is carried out for

10 classes; maximum classes that a FHR dataset can hold.

In [34], the best classification results were achieved

using a combination of conventional and nonlinear features

with sensitivity of 73.4 %, specificity of 76.3 % and Ta

ble

3V

alu

eso

fd

iag

no

stic

mea

sure

sfo

rth

eU

S-P

SO

-RR

red

uct

set

Dia

gn

ost

icm

easu

res

Cla

sses

Av

erag

e

Cal

m

slee

p

RE

M

slee

p

Cal

m

vig

ilan

ce

Act

ive

vig

ilan

ce

Sh

ift

pat

tern

Str

ess

situ

atio

n

Vag

al

stim

ula

tio

n

Lar

gel

yd

ecel

erat

ive

pat

tern

Pat

ho

log

ical

stat

e

Su

spec

t

pat

tern

Acc

ura

cy9

0.5

59

3.4

69

8.0

29

8.0

29

7.2

79

5.7

79

5.6

79

7.7

99

8.4

99

5.2

59

6.0

3

Sen

siti

vit

y7

8.9

18

8.4

35

8.4

96

5.4

34

1.6

78

5.2

48

3.3

37

7.5

77

2.4

67

5.6

37

2.7

2

Sp

ecifi

city

93

.11

95

.35

99

.04

99

.32

99

.22

97

.71

97

.33

98

.86

99

.37

97

.25

97

.66

Geo

met

ric

mea

n(G

M)

of

sen

siti

vit

yan

dsp

ecifi

city

85

.72

91

.82

76

.11

80

.61

64

.39

1.2

69

0.0

68

7.5

78

4.8

68

5.7

78

3.8

1

PP

V7

1.6

38

7.6

76

0.7

87

9.1

65

.22

87

.35

80

.77

78

.37

9.3

77

3.7

67

6.4

0

NP

V9

5.2

49

5.6

59

8.9

49

8.6

49

7.9

89

7.2

89

7.7

59

8.8

19

9.0

89

7.5

19

7.6

9

Geo

met

ric

mea

no

fP

PV

and

NP

V

82

.69

1.5

87

7.5

58

8.3

37

9.9

49

2.1

88

8.8

58

7.9

68

8.6

88

4.8

18

6.2

5

Pre

cisi

on

71

.63

87

.67

60

.78

79

.16

5.2

28

7.3

58

0.7

77

8.3

79

.37

73

.76

76

.40

Rec

all

78

.91

88

.43

58

.49

65

.43

41

.67

85

.24

83

.33

77

.57

72

.46

75

.63

72

.72

F-m

easu

re0

.75

09

0.8

80

50

.59

62

0.7

16

20

.50

85

0.8

62

80

.82

03

0.7

79

30

.75

76

0.7

46

90

.74

19

Neural Comput & Applic

123

F-measure of 71.9 %. In this paper, unsupervised PSO-

based relative reduct (US-PSO-RR) approach is applied for

FHR dataset, and it produces sensitivity of 72.72 %,

specificity of 97.66 % and F-measure of 74.19 %, which is

remarkable.

Features selected by US-PSO-RR, PCA and USRR

method are classified using single decision tree [64],

multilayer perceptron (MLP) neural network [65], prob-

abilistic neural network (PNN) [66] and random forest

[67]. Average of these measures is shown separately in

Fig. 4.

K-means clustering is also applied to check the error

rate produced by the reduct set. Since we have taken 10

classes for classification, we considered 3 clusters for

clustering. Features selected by the proposed method is

clustered and compared with the unreduced set of features

to ensure the compactness or separation of the clusters as

shown in Fig. 5. If the error generated is lesser, then it can

be concluded that the minimum set of features alone can be

taken for further treatment or drug discovery. Performance

of clustering is analyzed by root mean square error

(RMSE) and by mean absolute error (MAE) [68]. Xie–Beni

index (XB) [69] is applied to find the compactness and

separation of clusters produced by the feature selection

algorithms. Fitness of cluster is evaluated using Davies–

Bouldin index (DB) [70].

Overall importance of the variables selected by the three

different feature selection techniques and classified using

the above mentioned classifiers are also shown in Figs. 6, 7

and 8. Tables 3, 4 and 5 shows the values of diagnostic

measures produced by single decision tree. The values in

the tables show the accuracy of the selected features to

classify based on the classes.

According to the classifiers, number of severe deceler-

ations per second (DS) is the least important feature. The

most important feature is number of accelerations per

second (AC) and number of light decelerations per second

(DL) from the features selected by PCA as shown in Fig. 6.

Overall importance of the variables is computed for the

features selected by USRR algorithm. According to the

0.0000

0.2000

0.4000

0.6000

0.8000

PCA USRR US-PSO-RR

F-Measure

F-Measure

Fig. 3 F-measure for the reduced sets

0.0010.0020.0030.0040.0050.0060.0070.0080.0090.00

100.00Average of Diagnostic Measures

PCA USRR US-PSO-RR

Fig. 4 Average of diagnostic measures

0

0.05

0.1

0.15

0.2

0.25

0.3

RMSE MAE DB XB

Unreduced Set

PCA - ReductSet

USRR Set

US-PSO-RRReduct Set

Fig. 5 Cluster validity measures

0

20

40

60

80

100

LB AC FM UC DL DS DP

Overall Importance of Variables selected by PCA

Single Decision Tree MLP PNN Random Forest

Fig. 6 Overall importance of variables selected by PCA

Neural Comput & Applic

123

classifiers, FHR base line—number of heart beats per

second, mean and variance of the histogram are significant.

Number of histogram zeros—Nzeros and number of his-

togram peaks—Nmax are least important features as shown

in Fig. 7.

By applying US-PSO-RR algorithm, it is observed that

most of the features are equally important as shown in

Fig. 8, and hence, the accuracy of these features is high

compared to PCA and USRR methods.

6 Conclusion

In this paper, unsupervised PSO-based relative reduct

algorithm was applied for finding the most informative

features in the fetal heart rate, and the accuracy of the

features are evaluated using the diagnostic measures like

specificity, sensitivity, PPV, NPV, precision, recall,

F-score and geometric mean of specificity and sensitivity.

These measures prove that only 13 features selected by the

US-PSO-RR method are sufficient for classifying the 10

classes. We have carried out class-wise examination to

make the analysis specific. It is observed that 13 features

reducted by the proposed approach are capable of identi-

fying the classes with high accuracy. Though the three

class analysis can be made for the features selected, we

have analyzed classification accuracy for the maximum

number of classes that a fetal heart dataset can hold, and

the accuracy of the reduct set is also highly prominent.

Clustering accuracy is also measured; both the classifica-

tion and clustering accuracies suggest the reduct set pro-

duced by the proposed method can be taken for further

analysis.

Assessment of the fetal state completely depends on the

clinician’s experience, in such case less number of features

with high importance will help them to evaluate the state of

fetus and to stay away from risk. The limitation of this

algorithm is that it is sensitive to the input parameters such

as random position and random velocity. In case of ran-

domness, not all features will be given equal importance in

the first iteration itself. So the algorithm may take con-

siderable time to generate a reduct set with highest accu-

racy. In reality, the algorithm is scalable and time efficient,

as it takes all possible combinations to find the best reduct

0

10

20

30

40

50

60

70

80

90

100

Overall Importance of Variables selected by USRR

SDT

MLP

PNN

RandomForest

Fig. 7 Overall importance of

variables selected by USRR

0102030405060708090

100

Overall Importance of Variables selected by US-PSO-RR

SDT

MLP

PNN

Random Forest

Fig. 8 Overall importance of

variables selected by US-PSO-

RR

Neural Comput & Applic

123

Ta

ble

4V

alu

eso

fd

iag

no

stic

mea

sure

sfo

rP

CA

red

uct

set

Dia

gn

ost

icm

easu

res

Cla

sses

Av

erag

e

Cal

m

slee

p

RE

M

slee

p

Cal

m

vig

ilan

ce

Act

ive

vig

ilan

ce

Sh

ift

pat

tern

Str

ess

situ

atio

n

Vag

al

stim

ula

tio

n

Lar

gel

yd

ecel

erat

ive

pat

tern

Pat

ho

log

ical

stat

e

Su

spec

t

pat

tern

Acc

ura

cy8

7.0

69

2.3

89

7.5

19

7.5

59

5.3

49

5.6

79

6.4

79

7.8

89

6.7

19

1.6

79

4.8

2

Sen

siti

vit

y7

58

9.9

81

.89

45

.68

5.5

68

4.3

48

4.9

27

8.5

42

.03

59

.95

6.7

8

Sp

ecifi

city

89

.72

93

.28

99

.95

99

.61

98

.49

97

.77

98

.03

98

.91

98

.54

94

.92

96

.92

Geo

met

ric

mea

n(G

M)

of

sen

siti

vit

yan

dsp

ecifi

city

82

.03

91

.62

13

.73

67

.45

23

.39

90

.81

91

.24

88

.12

64

.36

75

.46

8.8

2

PP

V6

1.6

78

3.3

65

08

2.2

21

1.4

38

7.5

85

.26

79

.25

49

.15

54

.63

64

.45

NP

V9

4.2

19

6.1

49

7.5

59

7.8

99

6.7

59

7.1

29

7.9

79

8.8

69

8.0

69

5.8

69

7.0

4

Geo

met

ric

mea

no

fP

PV

and

NP

V7

6.2

28

9.5

26

9.8

48

9.7

13

3.2

59

2.1

89

1.4

88

.51

69

.43

72

.37

77

.24

Pre

cisi

on

61

.67

83

.36

50

82

.22

11

.43

87

.58

5.2

67

9.2

54

9.1

55

4.6

36

4.4

5

Rec

all

75

89

.98

1.8

94

5.6

85

.56

84

.34

84

.92

78

.54

2.0

35

9.9

56

.78

F-m

easu

re0

.67

69

0.8

65

40

.03

64

0.5

87

30

.07

48

0.8

58

90

.85

09

0.7

88

70

.45

31

0.5

71

40

.57

64

Ta

ble

5V

alu

eso

fd

iag

no

stic

mea

sure

sfo

rU

SR

Rre

du

ctse

t

Dia

gn

ost

icm

easu

res

Cla

sses

Av

erag

e

Cal

m

slee

p

RE

M

slee

p

Cal

m

vig

ilan

ce

Act

ive

vig

ilan

ceS

hif

t

pat

tern

Str

ess

situ

atio

n

Vag

al

stim

ula

tio

n

Lar

gel

yd

ecel

erat

ive

pat

tern

Pat

ho

log

ical

stat

e

Su

spec

t

pat

tern

Acc

ura

cy9

1.3

83

.44

97

.79

97

.74

96

.28

88

.38

91

.39

98

.92

98

.87

96

.89

4.0

9

Sen

siti

vit

y7

5.7

87

5.1

35

4.7

25

3.0

92

56

9.8

85

3.5

78

6.9

27

8.2

68

5.2

86

5.7

6

Sp

ecifi

city

94

.72

86

.55

98

.89

99

.51

98

.78

91

.81

96

.48

99

.55

99

.56

97

.98

96

.38

Geo

met

ric

mea

n(G

M)

of

sen

siti

vit

yan

dsp

ecifi

city

84

.72

80

.64

73

.56

72

.68

49

.69

80

.17

1.8

99

3.0

28

8.2

79

1.4

17

8.6

0

PP

V7

5.9

86

7.6

55

5.7

78

1.1

34

1.8

66

1.2

16

7.1

69

1.1

88

5.7

18

1.1

67

0.8

8

NP

V9

4.6

69

0.2

99

8.8

49

8.1

79

7.4

19

4.2

89

3.9

29

9.3

19

9.2

79

8.4

99

6.4

6

Geo

met

ric

mea

no

fP

PV

and

NP

V

84

.81

78

.16

74

.25

89

.24

63

.86

75

.97

79

.42

95

.16

92

.24

89

.41

82

.25

Pre

cisi

on

75

.98

67

.65

55

.77

81

.34

1.8

66

1.2

16

7.1

69

1.1

88

5.7

18

1.1

67

0.9

0

Rec

all

75

.78

75

.13

54

.72

53

.09

25

69

.88

53

.57

86

.92

78

.26

85

.28

65

.76

F-m

easu

re0

.75

88

0.7

11

90

.55

24

0.6

41

80

.31

30

.65

26

0.5

96

0.8

90

.81

82

0.8

31

70

.67

66

Neural Comput & Applic

123

set rather than taking the dataset as such. As a future

direction, neural networks can be used to tune the param-

eters and to achieve better performance.

References

1. Geijn HP (1996) Developments in CTG analysis. Bailliere’s Clin.

Obstet. Gynaecol. 10(2):185–209

2. Steer PJ (2008) Has electronic fetal heart rate monitoring made a

difference. Semin. Fetal Neonat. Med. 13:2–7

3. FIGO (1986) Guidelines for the use of fetal monitoring. Int.

J. Gynecol. Obstet. 25:159–167

4. Goncalves H, Rocha AP, De Campos DA, Bernardes J (2006)

Linear and nonlinear fetal heart rate analysis of normal and aci-

demic fetuses in the minutes preceding delivery. Med. Biol. Eng.

Comput. 44(10):847–855

5. Magenes G, Signorini MG, Arduini D (2000) Classification of

cardiotocographic records by neural networks. In: Proceedings of

the IEEE-INNS-ENNS international joint conference on neural

networks IJCNN, vol 3, pp 637–641

6. Salamalekis E, Thomopoulos P, Giannaris D, Salloum I, Vasios

G, Prentza A, Koutsouris D (2002) Computerised intrapartum

diagnosis of fetal hypoxia based on fetal heart rate monitoring

and fetal pulse oximetry recordings utilising wavelet analysis and

neural networks. BJOG 109(10):1137–1142

7. Georgoulas G, Stylios C, Groumpos P (2005). Classification of

fetal heart rate using scale dependent features and support vector

machines. In: Proceedings of 16th IFAC world congress

8. Leski J (2003) Neuro-fuzzy system with learning tolerant to

imprecision. Fuzzy Sets Syst 138:427–439

9. Czabanski R, Jezewski M, Wrobel J, Horoba K, Jezewski J

(2008). A neurofuzzy approach to the classification of fetal car-

diotocograms. In: Proceedings of 14th international conference

NBC2008, vol 20, pp 446–449

10. Czabanski R, Jezewski M, Wrobel J, Jezewski J, Horoba K

(2010) Predicting the risk of low-fetal birth weight from cardi-

otocographic signals using ANBLIR system with deterministic

annealing and e-insensitive learning. IEEE Trans Inf Technol

Biomed 14:1062–1074

11. Vapnik V (1998) Statistical learning theory. Wiley, New York

12. Vapnik V (1999) An overview of statistical learning theory. IEEE

Trans Neural Netw 10:988–999

13. Magenes G, Signorini MG, Arduini D (2000) Classification of

cardiotocographic records by neural networks. In: Proceedings of

the IEEE-INNSENNS international joint conference on neural

networks (IJCNN’00), vol 3, pp 637–641

14. Magenes G, Signorini MG, Sassi R, Arduini D (2001) Multi-

parametric analysis of fetal heart rate: comparison of neural and

statistical classifiers. In: 9th mediterranean conference on medical

and biological engineering and computing (MEDICON 2001),

IFMBE Proceedings, 12–15 June 2001, Pula, Croatia, vol 1,

pp 360–363

15. Georgoulas G, Stylios C, Bernardes J, Groumpos PP (2004)

Classification of cardiotocograms using Support Vector

Machines. In: Proceedings 10th IFAC symposium on large scale

systems: theory and applications (LSS’04), 26–28 July 2004,

Osaka, Japan

16. Pawlak Z (1982) Rough sets. Int J Comput Inform Sci 11(5):

341–356

17. Nizar Banu PK, Hannah Inbarani H (2012) Performance evalu-

ation of hybridized rough set based unsupervised approaches for

gene selection. Int J Comput Intell Inf 2(2):132–141

18. Costa A, Ayres-de-Campos D, Costa F, Santos C, Bernardes J

(2009) Prediction of neonatal acidemia by computer analysis of

fetal heart rate and ST event signals. Am J Obstet Gynecol

201(5):464.e1–464.e6

19. Georgoulas G, Stylios CD, Groumpos PP (2006) Feature

extraction and classification of fetal heart rate using wavelet

analysis and support vector machines. Int J Artif Intell Tools

15(3):411–432

20. Georgoulas G, Stylios C, Groumpos P (2006) Predicting the risk

of metabolic acidosis for newborns based on fetal heart rate

signal classification using support vector machines. IEEE Trans

Biomed Eng 53(5):875–884

21. Warrick P, Hamilton E, Precup D, Kearney R (2010) Classification

of normal and hypoxic fetuses from systems modeling of intra-

partum cardiotocography. IEEE Trans Biomed Eng 57(4):771–779

22. Chaffin DG, Goldberg CC, Reed KL (1991) The dimension of chaos

in the fetal heart rate. Am J Obstet Gynecol 165(4):1425–1429

23. Gough NA (1993) Fractal analysis of foetal heart rate variability.

Physiol Meas 14(3):309–315

24. Felgueiras CS, Marques de Sa JP, Bernardes J, Gama S (1998)

Classification of foetal heart rate sequences based on fractal

features. Med Biol Eng Comput 36(2):197–201

25. Kikuchi A, Unno N, Horikoshi T, Shimizu T, Kozuma S, Take-

tani Y (2005) Changes in fractal features of fetal heart rate during

pregnancy. Early Hum Dev 81(8):655–661

26. Georgoulas G, Gavrilis D, Tsoulos IG, Stylios C, Bernardes J,

Groumpos PP (2007) Novel approach for fetal heart rate classi-

fication introducing grammatical evolution. Biomed Signal Pro-

cess Control 2(2):69–79

27. Van Laar J, Porath M, Peters C, Oei S (2008) Spectral analysis of

fetal heart rate variability for fetal surveillance: review of the

literature. Acta Obstet Gynecol Scand 87(3):300–306

28. Van Laar J, Peters CHL, Houterman S, Wijn PFF, Kwee A, Oei

SG (2011) Normalized spectral power of fetal heart rate vari-

ability is associated with fetal scalp blood pH. Early Hum Dev

87(4):259–263

29. Hopkins P, Outram N, Zofgren N, Ifeachor EC, Rosen KG (2006)

A comparative study of fetal heart rate variability analysis tech-

niques. In: Proceedings of the 28th annual international confer-

ence of the ieee engineering in medicine and biology society,

pp 1784–1787

30. Pincus SM, Viscarello RR (1992) Approximate entropy: a regu-

larity measure for fetal heart rate analysis. Obstet Gynecol

79(2):249–255

31. Ferrario M, Signorini MG, Magenes G, Cerutti S (2006) Com-

parison of entropy based regularity estimators: application to the

fetal heart rate signal for the identification of fetal distress. IEEE

Trans Biomed Eng 53(1):119–125

32. Goncalves H, Bernardes J, Rocha AP, Ayres-de-Campos D

(2007) Linear and nonlinear analysis of heart rate patterns asso-

ciated with fetal behavioral states in the antepartum period. Early

Hum Dev 83(9):585–591

33. Ferrario M, Signorini M, Magenes G (2009) Complexity analysis

of the fetal heart rate variability: early identification of severe

intrauterine growth-restricted fetuses. Med Biol Eng Comput

47(9):911–919

34. Spilka J, Chudacek V, Koucky M, Lhotska L, Huptych M, Janku

P, Georgoulas G, Stylios C (2012) Using nonlinear features for

fetal heart rate classification. Biomed Signal Process Control

7(4):350–357

35. Questier F, Rollier IA, Walczak B, Massart DL (2002) Appli-

cation of rough set theory to feature selection for unsupervised

clustering. Chemometr Intell Lab Syst 63(2):155–167

36. Zhang J, Wang J, Li D, He H, Sun J (2003) A new heuristic

reduct algorithm base on rough sets theory. LNCS, vol 2762,

pp 247–253. Springer, Berlin

Neural Comput & Applic

123

37. Swiniarski RW, Skowron A (2003) Rough set methods in feature

selection and recognition. Pattern Recognit Lett 24(6):833–849

38. Thangavel K, Pethalakshmi A (2009) Dimensionality reduction

based on rough set theory: a review. Appl Soft Comput 9(1):1–12

39. Echeverrıa JC, 9Alvarez-Ramırez J, Pena MA, Rodrıguez E,

Gaitan MJ, Gonzalez-Camarena R (2012) Fractal and nonlinear

changes in the long-term baseline fluctuations of fetal heart rate.

Med Eng Phys 34(4):466–471

40. Skinner J, Garibaldi J, Ifeachor E (1999) A fuzzy system for fetal

heart rate assessment. In: Reusch B (ed) Computational intelli-

gence. Lecture notes in computer science, vol 1625. Springer,

Berlin, pp 20–29

41. Skinner J, Garibaldi J, Curnow J, Ifeachor E (2000) Intelligent

fetal heart rate analysis. In: 1st International conference on

advances in medical signal and information processing, pp 14–21

42. Keith R, Beckley S, Garibaldi J (1995) A multicentre compara-

tive study of 17 experts and an intelligent computer system for

managing labour using the cardiotocogram. Br J Obstet Gynaecol

102(9):688–700

43. Signorini M, de Angelis A, Magenes G, Sassi R, Arduini D,

Cerutti S (2000). Classification of fetal pathologies through fuzzy

inference systems based on a multiparametric analysis of fetal

heart rate. In: Computers in cardiology, pp 435–438. Cambridge,

MA

44. Arduini D, Giannini F, Magnes G, Signorini MG, Meloni P

(2001). Fuzzy logic in the management of new prenatal variables.

In: Proceedings of 5th world congress of perinatal medicine,

Barcelona, vol 1, pp 1211–1216

45. Huang YP, Huang YH, Sandnes FE (2006) A fuzzy inference

method-based fetal distress monitoring system. In: IEEE inter-

national symposium on industrial electronics, vol 1, pp 55–60,

9–13 July 2006, Montreal, Que

46. Hasbargen U (1994) Application of neural networks for intra-

partum surveillance. In: van Geijn H, Copray F (eds) A critical

appraisal of fetal surveillance. Elsevier Science (Excerpta Med-

ica), Amsterdam, New York, pp 363–367

47. Beksac M, Ozdemir K, Erkmen A, Karakas U (1994) Assessment

of antepartum fetal heart rate tracings using neural networks. In:

van Geijn H, Copray F (eds) A critical appraisal of fetal sur-

veillance. Elsevier Science (Excerpta Medica), Amsterdam, New

York, pp 354–362

48. Magenes G, Signorini M G, Arduini D (2000) Classification of

cardiotocographic records by neural networks. In: Proceedings

IEEE-INNS-ENNS international joint conference on neural net-

works IJCNN, vol 3, pp 637–641

49. Magenes G, Signorini M, Sassi R, Arduini D (2001). Multi-

parametric analysis of fetal heart rate: comparison of neural and

statistical classifiers. In: IFMBE proceedings of MEDICON, vol

1, pp 360–363

50. Noguchi Y, Matsumoto F, Maed K, Nagasawa T (2009) Neural

network analysis and evaluation of the fetal heart rate. Algo-

rithms 2:19–30

51. Jezewski M, Wrobel J, Horoba K, Gacek A, Henzel N, Leski J

(2007) The prediction of fetal outcome by applying neural net-

work for evaluation of CTG records. In: Kurzynski M, Puchala E,

Wozniak M, Zolnierek A (eds) Computer recognition systems 2.

Advances in intelligent and soft computing, vol 45. Springer,

Berlin, pp 532–541

52. Jezewski M, Czabanski R, Wrobel J, Horoba K (2010) Analysis

of extracted cardiotocographic signal features to improve

automated prediction of fetal outcome. Biocybernetics and Bio-

medical Engineering 30:39–47

53. Frize M, Ibrahim D, Seker H, Walker R, Odetayo M, Petrovic D,

Naguib R (2004) Predicting clinical outcomes for newborns using

two artificial intelligence approaches. In: Engineering in medi-

cine and biology society, IEMBS’04. Proceedings of 26th annual

international conference of the IEEE, vol 2, pp 3202–3205

54. Azar AT, Nizar Banu PK, Hannah Inbarani H (2013) PSORR—

An unsupervised feature selection technique for fetal heart rate.

In: Proceedings of the 5th international conference on modelling,

Identification and control (ICMIC 2013), Aug 31–Sept 2 2013.

Cairo, Egypt, pp 60–65

55. Komorowski J, Pawlak Z, Polkowski L, Skowron A (1999)

Rough sets: a tutorial. In: Pal SK, Skowron A (eds) Rough fuzzy

hybridization: a new trend in decision making. Springer, Berlin,

pp 3–98

56. Kalyani P, Karnan M (2011) A new implementation of attribute

reduction using quick relative reduct algorithm. Int J Internet

Comput 1(1):99–102

57. Lin TY, Cercone N (1997) Rough sets and data mining: analysis

of imprecise data. Kluwer Academic Publishers, Dordrecht

58. Hu XT, Lin TY, Han J (2003) A new rough sets model based on

database systems. In: Rough sets, fuzzy sets, data mining, and

granular computing. Lecture notes in computer science, vol 2639,

pp 114–121. doi:10.1007/3-540-39205-X_15

59. Hannah Inbarani H, Nizar Banu PK (2012) Unsupervised hybrid

PSO—relative reduct approach for feature reduction. In: Pro-

ceedings of the international conference on pattern recognition,

informatics and medical engineering (PRIME), pp 103–108.

doi:10.1109/ICPRIME.2012.6208295

60. Velayutham C, Thangavel K (2011) Unsupervised feature

selection using rough sets. In: Proceedings of the international

conference-emerging trends in computing, pp 307–314

61. Velayutham C, Thangavel K (2011) Unsupervised quick reduct

algorithm using rough set theory. J Electron Sci Technol

9(3):193–201

62. Bache K, Lichman M (2013) UCI machine learning repository.

http://archive.ics.uci.edu/ml. University of California, School of

Information and Computer Science, Irvine, CA

63. Davis JC (2002) Statistics and data analysis in geology, 3rd edn.

Wiley, NewYork

64. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification

and regression trees. Wadsworth, Belmont, CA

65. Bridle JS (1989) Probabilistic interpretation of feedforward

classification network outputs, with relationships to statistical

pattern recognition. In: Fougelman-Soulie F (ed) Neurocomput-

ing: algorithms, architectures and applications. Springer, Berlin,

pp 227–236

66. Specht DF (1990) Probabilistic neural networks. Neural Netw

3(1):109–118

67. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

68. De Franca OF, Ferreira HM, Von Zuben FJ (2007) Applying

biclustering to perform collaborative filtering. In: Proceedings of

the seventh international

69. Xi XL, Beni G (1991) A validity measure for fuzzy clustering.

IEEE Trans Pattern Anal Mach Intell 13(8):841–847

70. Davies DL, Bouldin DW (1979) A cluster separation measure.

IEEE Trans Pattern Anal Mach Intell 1(2):224–227

Neural Comput & Applic

123