Comparing Different Settings of Parameters Needed for Pre-processing

of ECG Signals used for Blood Pressure Classiﬁcation

Monika Simjanoska

, Gregor Papa

, Barbara Korou

c Seljak

and Tome Eftimov

Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University,

Rugjer Boshkovikj 16, 1000 Skopje, Macedonia

Computer Systems Department, Jo

zef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia

Keywords:

Biomedical Signal Analysis, ECG, Signal Length, Baseline Removal, Blood Pressure Classiﬁcation.

Abstract:

Because a real-time monitoring using electrocardiogram (ECG) signals is a challenging task, the pre-

processing techniques used for ECG signal analysis are crucial for obtaining information that is further used

for some more complex analysis, such as predictive analyses. We compared different settings of parameters

needed for pre-processing of ECG signals in order to estimate the valuable information that can be further

used for blood pressure classiﬁcation. Two parameters were involved in the comparison: i) the signal length

used for ECG segmentation; and ii) the cut-off frequency used for baseline removal. The ﬁrst parameter is the

parameter used for obtaining ECG segments that are further used, and the second one is the frequency used for

baseline removal. Thirty different combinations, each a combination of a signal length and a cut-off frequency,

were evaluated using a dataset that contains data from ﬁve commercially available ECG sensors. For signal

lengths: 10 s, 20 s, and 30 s, were used for data segmentation, while the cut-off frequency for baseline removal

starts from 0.05 Hz, till 0.50 Hz, with a step length of 0.05 Hz. The evaluation of these combinations was done

in combination with complexity analysis used for features extraction that are further used for blood pressure

classiﬁcation. Experimental results, obtained using a data-driven approach by comparing the combinations

using the results obtained from the classiﬁcation for 17 performance measures, showed that a signal length

of 30 s carries the most information in a combination with cut-off frequency between 0.10 Hz and 0.20 Hz.

Results contribute to the arguments published in the literature discussing the optimal ECG sample lengths

needed for building predictive models, as well as the lower frequencies where the ECG components overlap

with the baseline wander noise.

1 INTRODUCTION

A biomedical processing system includes a biological

system of interest, sensors used to follow the physio-

logical conditions, and a methodology for biomedical

signal analysis in order to extract the useful informa-

tion from the system. In this paper, the biological sys-

tem addressed is the heart system, and its electrical

activity is followed using electrocardiogram (ECG)

signals. Through ECG we want to follow blood pres-

sure. Two phases for managing blood ﬂow exist: i) a

diastole phase known as the ﬁlling phase and ii) a sys-

tole phase known as the pumping phase. The blood

pressure (BP) is deﬁned as the force of the blood

pushing against the walls of the arteries as the heart

pumps blood (Shriram et al., 2010).

Nowadays, commercially available wearable bio-

sensors (e.g., ECG sensors, sweating-rate sensors,

respiration-rate body sensors) provide an opportunity

for real-time monitoring of human vital signs, which

further helps the process of preventive, timely notiﬁ-

cation and real-time diagnosis (Cosoli et al., 2015).

Recently, many systems for non-invasive BP moni-

toring have been developed, such as: i) the Superﬁ-

cial Temporal Artery Tonometry-based device (Sackl-

Pietsch, 2010), ii) the PPG optical sensor (Canning

et al., 2016), iii) ARTSENS (ARTerial Stiffness Eval-

uation for Non-invasive Screening) for brachial ar-

terial pressure (Mouradian et al., 2015), iv) an elec-

tronic system based on the oscillometric method (Sa-

hani et al., 2014), v) a BP estimation device based

on the principle of volume compensation (Marani

and Perri, 2012), vi) a Modulated Magnetic Signa-

ture of Blood mechanism (Tanaka et al., 2007), and

vii) portable equipment that includes a cuff-based BP

sensing system (Li et al., 2013). However, they are

specialized only for BP measurements, and exclude

Simjanoska, M., Papa, G., Seljak, B. and Eftimov, T.

Comparing Different Settings of Parameters Needed for Pre-processing of ECG Signals used for Blood Pressure Classiﬁcation.

DOI: 10.5220/0007390100620072

In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 62-72

ISBN: 978-989-758-353-7

the other vital signs.

The data collected from sensors that measure ECG

signal as physiological signal can be further used

for developing smart phone applications for real-

time diagnosis (Cosoli et al., 2015). In (Simjanoska

et al., 2018), the relation between the ECG signal

and BP using supervised machine learning (ML) ap-

proaches has been investigated. For this reason, a pre-

processing that involves ECG signal complexity anal-

ysis was applied in order to extract features that are

further used for training a meta-model used for blood

pressure classiﬁcation. The ECG signal analysis was

performed using a signal length of 30 s and a band-

pass Butterworth ﬁlter with a frequency of 0.30 Hz for

removing the baseline wander without deforming the

ECG signal. These parameters (30 s; 0.03 Hz) were

chosen following the state-of-the-art literature. Using

such setting, promising results were achieved that in-

dicate the relation between the complexity features of

the ECG signals and the BP.

However, in real-life scenarios (e.g., civil and mil-

itary emergency situations) there is no guaranty that

there will be enough time to measure long ECG sam-

ples. For this reason, in this study we focus on com-

parison of different combinations of parameters for

signal length and frequency used for baseline removal

(i.e. different ECG signal analysis) and their inﬂuence

on the end result of the methodology used for BP clas-

siﬁcation. We consider signal lengths of 10 s, 20 s,

and 30 s, and frequency starting from 0.05 Hz, till

0.50 Hz, with a step length of 0.05 Hz. The novelty

is also in the evaluation of the inﬂuence, in which we

did not use only one performance measure, but a set

of performance measures with a data-driven approach

in order to make a general conclusion.

In the remainder of the paper, we ﬁrst present an

overview of the related work. Then we present the

methodology used for comparison of the parameters

values used for pre-processing of ECG signals is ex-

plained in detail, followed by the experimental results

and discussion. Finally, the conclusions of the paper

are presented followed by directions for future work.

2 RELATED WORK

Most of the recently published studies on BP esti-

mation use a combination of ECG and photoplethys-

mogram (PPG) sensors (Sahoo et al., 2011; Ilango

and Sridhar, 2014; Thomas et al., 2016). The com-

mon techniques for obtaining measurements on BP

mainly rely on Pulse Wave Velocity (PWV), Pulse Ar-

rival Time (PAT), and Pulse Transit Time (PTT) (Choi

et al., 2013; Goli and Jayanthi, 2014; Nye et al., 2015;

Seo et al., 2015), all of which require an accurate

PPG measurement, which is not simple to get unob-

trusively yet, and no clear proof on the PPG measure-

ment’s relation to the BP has been provided (Payne

et al., 2006; Wong et al., 2009; i Car

os, 2011). The

relationship between the ECG signal and BP estima-

tion has been discussed in (Chan et al., 2001; Ah-

mad et al., 2012). Both methods used an additional

PPG sensor with an ECG sensor. The results from

several studies that address the ECG-BP relationship

(Schroeder et al., 2003; Hassan et al., 2008) conﬁrm

that no strong relationship exists between hyperten-

sion occurrence and morphological changes in ECG.

For this reason, in (Simjanoska et al., 2018), instead

of using morphological changes in ECG, complexity

analysis was used to detect bio-system complexity.

For this reason, the ECG signals were segmented us-

ing signal length of 30 s, for which cut-off frequency

of 0.30 Hz was applied for baseline removal.

3 METHODOLOGY

To compare different combinations of parameters

used for pre-processing of ECG signals, we propose

an approach, which goes through four different lev-

els: data segmentation and baseline removal, feature

extraction, classiﬁcation, and classiﬁers ranking.

• Level 1: The key part is the data segmentation

and baseline removal, because the end result

is comparing different parameters that are used

for obtaining ECG segments that are further used

for blood pressure classiﬁcation. For this reason,

30 different combinations were included, a sig-

nal length of 10 s, 20 s, and 30 s, and frequency

for baseline removal starting from 0.05 Hz, till

0.50 Hz, with a step length of 0.05 Hz.

• Level 2: To compare the different combinations,

for each of them the same complexity analysis

was applied for feature extraction.

• Level 3: The extracted features for each combi-

nation are further used to learn a classiﬁcation

model for blood pressure following the stacking

procedure.

• Level 4: Finally, by performing classiﬁers rank-

ing using a set of different performance measures,

the inﬂuence of each combination can be esti-

mated.

The ﬂowchart of the methodology is presented in Fig-

ure 1. Further, each level is explained in more detail.

Comparing Different Settings of Parameters Needed for Pre-processing of ECG Signals used for Blood Pressure Classiﬁcation

Raw ECG signal

Data

Segmentation

Combination 1 Combination n

. . .

Feature

Extraction

Data set 1 Data set n

Classiﬁcation

. . .

Classiﬁer 1 Classiﬁer n

Classiﬁers

Ranking

Best

Combination

Level 2

Level 1

Level 3

Level 4

Figure 1: The ﬂowchart of the methodology.

3.1 Data Segmentation and Baseline

Removal

The raw ECG signals are segmented using signal

lengths of 10 s, 20 s, and 30 s. In the process of seg-

mentation, the segments were also labeled with the

appropriate BP class. The result of this are three data

sets, the ﬁrst consists of segments of 10 s, the sec-

ond consists of segments of 20 s, and the third of seg-

ments of 30 s. Further, a band-pass ﬁlter is applied on

each of these datasets in order to preserve only valid

ECG information. The cut-off frequency for base-

line removal starts from 0.05 Hz, till 0.50 Hz, with

a step of 0.05 Hz. Performing 10 cut-off frequencies

on each dataset, it results in 30 data sets, which were

further called “combinations” because each of them

is described with a combination of signal length and

cut-off frequency.

3.2 Feature Extraction

After creating 30 datasets (combinations), each of

them is used for feature extraction, in which the prob-

lem is addressed from the perspective of complex-

ity analysis (Eke et al., 2002; Morabito et al., 2012;

McBride et al., 2014), and not as other traditional

approaches in which morphogical features are used

(Wong et al., 2009; Zhang and Zhang, 2006; Nitzan,

2011). For this reason, for each dataset, ﬁve com-

plexity metrics are calculated: signal mobility, signal

complexity, fractal dimension, autocorrelation, and

entropy.

3.2.1 Signal Mobility

Given x

, i = 1,. . . ,N is the ECG signal of length N

and d

= x

j+1

− x

is the ﬁrst-order variation in the

signal, then the ﬁrst-order factors, S

and S

, are cal-

culated as:

∑

i=1

(1)

∑

N−1

j=2

N − 1

(2)

The signal mobility quantitatively measures the

level of variation in the signal. It is calculated as a

ratio between the factors S

and S

Mobility =

, (3)

3.2.2 Signal Complexity

Given the ﬁrst-order variation of the ECG signal d

j = 1, . . . ,N −1, the second-order variation of the sig-

nal is presented by g

= d

k+1

− d

. Then, the second-

order factor is calculated as:

∑

N−2

k=3

N − 2

, (4)

Both the signal mobility and signal complex-

ity were computed by using the Hjorth parameters

method (Kugiumtzis and Tsimpiris, 2010).

BIOSIGNALS 2019 - 12th International Conference on Bio-inspired Systems and Signal Processing

3.2.3 Fractal Dimension

Fractal dimension measures the self-similarity of the

signal. By zooming and comparing different portions,

it describes fundamental patterns hidden in the signal.

Higuchi algorithm (Monge-

Alvarez, 2015) has been

used to calculate the fractal dimension. The method

works with a set of k subseries with different resolu-

tions, creating a new time series X

, for m = 1,. . . , k:

: x(m),x(m + k),x(m + 2k),...,x(m + b

N − m

ck)

(5)

The length of the curve X

, l(k) is calculated as:

l(k) =

(

∑

bN−m/kc

i=1

|x(m + ik) − x(m + (i − 1)k)|(N − 1))

N−m

c)k

(6)

Then, for each k in range 1 to k

max

, the average

length is calculated as the mean of the k lengths l(k)

for m = 1, ...k. The fractal dimension is the estimation

of the slope of the plot ln(l(k)) vs. ln(1/k).

3.2.4 Autocorrelation

The similarity between the signal and its shifted ver-

sion is measured via autocorrelation. Let τ be the

amount of shift, and then the autocorrelation is cal-

culated as:

(τ) =

+inf

−inf

x(t)x(t − τ)p

(x(t),x(t − τ))dt, (7)

where p

(x(t),x(t − τ)) presents the joint probability

density function of x(t) and x(t − τ).

3.2.5 Entropy

Entropy expresses the randomness of the signal. The

decrease of entropy often indicates a disease (Zhou

et al., 2017). The amount of information is expressed

through the concept of probability. Let p

denote the

probability of each outcome x

within the ECG signal

X for i = 1,. . . ,N − 1. Then, entropy is calculated as:

Entropy =

N−1

∑

i=0

log(

) (8)

3.3 Classiﬁcation

Performing the feature extraction for each of the 30

datasets (combinations), each of them is used to train

a meta-model that will be used to predict the BP class.

The performance of the meta-model on each data set

is used to compare different signal lengths and cut-

off frequencies. The methodology that is applied is

taken from a previous study (Simjanoska et al., 2018),

where the meta-model was learned for a signal length

of 30 s and cut-off frequency of 0.30 Hz. Using this

combination, it was shown that promising results can

be achieved.

The meta-model follows a stacking design, in

which seven different classiﬁers are used: Bagging

(Breiman, 1996), Boosting (Freund et al., 1996),

SVM (Hearst et al., 1998), K-means (Liao and Ve-

muri, 2002), Random Forest (Liaw et al., 2002),

Naive Bayes (Rish et al., 2001), and J48 (Patil and

Sherekar, 2013). The probabilities from each of them

for all the feature vectors to belong in each class, com-

prise a new feature vector upon which a META clas-

siﬁer (Random Forest) is trained. More details about

the classiﬁcation methodology are provided in (Sim-

janoska et al., 2018).

3.4 Ranking of Classiﬁers

For each dataset (combination), a meta-model is

trained using a training set and it is evaluated on a

corresponding validation set. For this reason, each

dataset is m-times split into training and validation

set, since we have multiple independent measure-

ment for each subject (i.e. participant) and as the

number of measurements varies for each subject.

We follow the rule that if a subject is included in one

set, none of its measurements may occur in another

set. So for each combination, m meta-models are

trained.

To evaluate the performance of the meta-models

trained for each combination of signal length and cut-

off frequency, we used an ensemble (a fusion) of a set

of 17 performance metrics:

• Accuracy (ACC) - represents the fraction be-

tween the number of correct predictions by the to-

tal number of predictions made.

• Cohen’s Kappa - compares the observed accu-

racy (the number of correctly classiﬁed instances)

with the expected accuracy (taking into account

the number of instances in each class, along with

the number of instances that the classiﬁer agreed

with the ground truth label).

• Precision (PR) - captures the effect of the large

number of negative examples on the classiﬁer’s

performance, by comparing false positives (FP)

to true positives (TP) rather than true negatives

(TN), i.e. it measures how many of the predicted

instances were TP.

• Recall (RC) - to measure how many of the TP

instances were predicted.

Comparing Different Settings of Parameters Needed for Pre-processing of ECG Signals used for Blood Pressure Classiﬁcation

• F-Measure - measures the trade-off between re-

call and precision giving equal importance to re-

call and precision.

• Area Under PRC - precision-recall curve does

not account for TN, since TN is not a component

of either Precision or Recall. Given there are more

negatives (normal) than positives (hypertension),

PRC might be a very informative evaluation met-

ric of the performance of the classiﬁer. Having

a model at the upper right corner, means that the

classiﬁer gets only the TP with no FP and no false

negatives (FN) at all and thus is a perfect classi-

ﬁer.

• Area Under ROC - the ROC curve plots the

True Positive Rate (TPR) vs. False Positive Rate

(FPR). Therefore achieving a model at the upper

left corner, means that the classiﬁers is getting no

FP at all and thus is a perfect classiﬁer.

• Matthews Correlation (COR) - takes the advan-

tage from all four metrics TP, FP, TN and FN to

calculate the correlation coefﬁcient between the

observed and predicted classiﬁcations (Vihinen,

2012).

• Relative Absolute Error (RAE) - is an error

measure relative to a simple predictor, meaning

it takes the total absolute error and normalizes it

by dividing it by the one of the simple predictor.

• Root Relative Squared Error (RRSE) - takes

the total squared error and normalizes it by divid-

ing by the total squared error of the simple predic-

tor. The square root reduces the error to the same

dimensions as the problem at hand.

• Root Mean Squared Error (RMSE) - explains

the standard deviation of the prediction error.

• Informedness (INF) - quantiﬁes how informed a

classiﬁer is for the speciﬁed class. It provides in-

sight into how consistently the classiﬁer predicts

the class (Powers, 2011).

• Markedness (MAR) - quantiﬁes how marked a

class is for the speciﬁed classiﬁer, i.e. how con-

sistently the class has the classiﬁer as a marker by

combining measures about correct classiﬁcations

(Powers, 2011).

• Micro F-measure (MF) - aggregates the contri-

butions of all classes to compute the average F-

measure by considering the total TPs, FNs and

FPs.

Table 1: Decision matrix.

. . . q

) q

) . . . q

)

) q

) . . . q

)

) q

) . . . q

)

• Log Likelihood (LL) - presents the probability of

the observed prediction given the real class.

• Mutual Information (MI) - measures whether

the real and the predicted labels are statistically

depended.

• Pearson’s Chi-squared Test (PRS) - to measure

whether the observed and expected frequencies

are the same by comparing the predicted contin-

gency table to an expected table.

To compare the results of the meta-models ob-

tained for each combination using a set of perfor-

mance measures, their results are organized into a

decision matrix (Table 1). The rows of the deci-

sion matrix correspond to different meta-models and

the columns correspond to the values obtained for

the performance measures. First, a generalized pref-

erence function should be selected for each perfor-

mance measure. In our case, the V -shape general-

ized preference function is used for each performance

measure, where the threshold of strict preference is

set to the maximum difference that exists for each

preference measure from all pairwise comparisons

according to that performance measure (Brans and

Mareschal, 2005). After that, the average preference

index for each pair of meta-models should be calcu-

lated, which gives information of global comparison

between them using all performance measures. To

rank the meta-models, a net ﬂow for each one needs

to be calculated. It is a difference between a posi-

tive preference ﬂow and a negative preference ﬂow of

the meta-model. The positive preference ﬂow gives

information about how a given meta-model is glob-

ally better than the other meta-models, while the neg-

ative preference ﬂow gives the information about how

a given meta-model is outranked by all the other meta-

models. This approach has already been used for

evaluation of multi-objective meta-heuristic stochas-

tic optimization algorithms regarding a set of perfor-

mance measures. More details about the ranking ap-

proach and the equations for the net, positive, and

negative ﬂow, can be found in (Eftimov et al., 2018).

BIOSIGNALS 2019 - 12th International Conference on Bio-inspired Systems and Signal Processing

4 EVALUATION

4.1 Data Collection from Multiple

Sensors

The data was collected using ﬁve ECG sensors, from

which four are commercially available ECG sensors

(Hacks, 2015; Biosignals, 2016; Technology, 2017;

Trobec et al., 2018a) and one is from the online avail-

able Physionet database (Goldberger et al., 2000). For

each ECG signal, reference SBP and DBP values are

attached, which are measured in parallel using elec-

tronic sphygmomanometer. The information about

each sensor, its reliability, the number of participants

measured by each sensor, and their age, is presented

in Table 2. All the human subjects involved in the re-

search signed an agreement allowing their ECG data

to be used for research goals. The involved partici-

pants come from different age and health status, and

the measurements were performed in moving and sit-

ting positions. The patients included from the Phy-

sionet database are explicitly selected to suffer brain

injuries.

As we mentioned before, each ECG signal is ac-

companied with SBP and DBP values, we also added

the BP class, which is the target in the classiﬁcation.

To obtain the BP class for each ECG measurement, a

publicly available scheme presented in Table 3 (Pro-

gram et al., 2004; AHA, 2016) was used. The BP

classes seem not to be mutually exclusive, however

this was solved by giving priority to the more se-

vere BP conditions by checking the “AND” condi-

tions at the end of the ECG samples mapping proce-

dure. To solve the imbalanced-class data, BP classes

were grouped into three main categories: hypotension

(HPTN) and normal (N) as Normal class (denoted as

0); prehypertension (PHTN) as Prehypertension class

(denoted as 1); and stage 1 hypertension (S1HTN),

stage 2 hypertension (S2HTN), isolated systolic hy-

pertension (ISHTN), and hypertensive crisis (HTNC)

as Hypertension class (denoted as 2).

4.2 Data Segmentation and Baseline

Removal

After the data from all sensors was collected and la-

beled with an appropriate BP class, data segmentation

was applied on each ECG signal. The raw ECG sig-

nals were segmented using signal lengths of 10 s, 20 s,

and 30 s, and each of the signal length was analyzed

with different cut-off frequency for baseline removal

starting from 0.05 Hz, till 0.50 Hz, with a step of

0.05 Hz. The segments were labeled with the BP class

from the corresponding ECG signal from which they

are obtained. Finally, we create 30 different combina-

tions that should be analyzed in the case of BP clas-

siﬁcation in order to provide information about which

pre-processing combination gives the most promising

results.

4.3 Feature Extraction

For each of 30 datasets (i.e. combinations), we ex-

tracted the ﬁve complexity features that will be further

used for learning a meta-model used for BP classiﬁca-

tion. Each of the datasets is described with the same

ﬁve features, however the features values are different

due to different signal length and cut-off frequency for

baseline removal that are used.

4.4 Classiﬁcation

Having the 30 datasets, for each one the classiﬁca-

tion was done using a previously published methodol-

ogy that follows a stacking design (Simjanoska et al.,

2018). Output probabilities from seven different clas-

siﬁers, already mentioned above, were used as fea-

tures to train a meta-model for multi-class BP classi-

ﬁcation using Random Forest. In our case, the number

of classes is three.

4.5 Ranking of Classiﬁers

To evaluate the performance of each meta-model ob-

tained for each dataset (combinations), we follow the

idea of splitting the datatsets into training and valida-

tion set. The splitting is made in a way that if a subject

is included in the training set, none of its measure-

ments may occur in the validation set, and vice versa.

For this reason and in order to build a robust model

for each combination, each dataset was split 30 times

into training and validation sets. We followed the rule

of 75% of the subjects were used for training and 25%

of the subjects were used for validation.

Using this approach, for each combination we ob-

tained 30 different meta-models and their evaluation

on the corresponding validation sets described by the

17 performance measures. The evaluation was per-

formed in two scenarios.

First, within each conﬁguration, we used the rank-

ing scheme based on PROMOTHEE method in or-

der to ﬁnd the best split for each combination, which

results in the best meta-model for each combina-

tion. Further, the best meta-models for all combi-

nations were again evaluated with the same ranking

scheme and the set of 17 performance measures in

order to obtain the most promising combination of

Comparing Different Settings of Parameters Needed for Pre-processing of ECG Signals used for Blood Pressure Classiﬁcation

Table 2: Sensors summary information.

Dataset Reliability Participants Age

Cooking hacks (Hacks, 2015) (Winderbank-Scott and Barnaghi, 2017) 16 16–72

180° eMotion FAROS (Biosignals, 2016) (Ahonen et al., 2016) 3 25–27

Zephyr Bioharness module (Technology,

2017)

(Johnstone et al., 2012) 14 20–73

Savvy sensor platform (Trobec et al.,

2018a)

(Trobec et al., 2018b) 21 15–54

Charis Physionet database (Goldberger

et al., 2000)

(Kim et al., 2016) 7 20–74

Table 3: Rules and categorization.

Joined class Class SBP (mmHg) Logical DBP (mmHg)

Normal

HPTN <=90 OR <=60

N 90–119 AND 60–79

Prehypertension PHTN 120–139 OR 80–89

Hypertension

S1HTN 140–159 OR 90–99

S2HTN >=160 OR >=100

ISHTN >=140 AND <90

HTNC >=180 OR >=110

pre-processing that was applied. The performance

of the best meta-model within each conﬁguration are

presented in Table 4. The ﬁrst column presents the

parameters settings for each combination. The last

column reported the ranking for each combination,

which is obtained by the ranking scheme when the

best meta-models from all combinations are com-

pared.

Using the information reported in Table 4, we can

see that the best results are obtained using the meta-

model for the combination with the signal length

of 10 s and cut-off frequency for baseline removal

0.30 Hz, the second best is obtained for the signal

length of 20 s and the cut-off frequency of 0.45 Hz,

and the third with the signal length of 30 s and the

cut-off frequency of 0.15 Hz. From here, there is no

general conclusion for which data segmentation pro-

vides the most valuable information for BP classiﬁ-

cation; however there is a question of the robustness

of the results, or if selecting the best meta-model (or

best splitting) for each combination corresponds to a

real-life application.

Second, in order to have more robust results, the

results from 30 splits for each combination were ag-

gregated by averaging the results for each perfor-

mance measure separately, and the average value for

each performance measure was further used for each

combination. This was made with the purpose of fol-

lowing the cross-validation idea. Then, the 30 com-

binations were compared using the ranking scheme

and the set of 17 performance measures. The aver-

aged performance for each conﬁguration is presented

in Table 5. The ﬁrst column presents the parameters

settings for each combination. The last column re-

ported the ranking for each combination, which is ob-

tained by the ranking scheme when all combinations

are compared using the averaged results.

Using the results reported in Table 5, it follows

that the best meta-model can be learned using the sig-

nal length of 30 s and cut-off frequency of 0.10 Hz,

the second best can be learned using the signal length

of 30 s and the cut-off frequency of 0.15 Hz, and the

third with the signal length of 30 s and the cut-off fre-

quency of 0.20 Hz. We can also concluded that the 10

best combinations are obtained when the signal length

is set at 30 s, so it carries the most valuable informa-

tion. Depending on the cut-off frequency that is used,

best models can be learned when it is between 0.10 Hz

and 0.20 Hz. As we mentioned before, in real-life sce-

narios (e.g., civil and military emergency situations),

maybe there is no time to obtain a signal of 30 s, so if

we have a signal length of 10 s, using the results re-

ported in Table 5, we can select the best combination

when the signal length is 10 s, and this combination

is obtained with a cut-off frequency of 0.25 Hz.

We need to mention that the focus here is made

on different settings of parameters for pre-processing

of ECG signals used for blood pressure classiﬁcation.

Regarding the classiﬁcation methodology, it could be

also tested with some other methodologies; we se-

lected one that was recently published. The novelty

in the comparison is that we used a set of 17 per-

formance measures instead of focusing of compar-

ing them separately to the most commonly used per-

formance measures in which a general conclusion is

made. The performance measures were selected man-

ually.

5 CONCLUSIONS

We investigated different parameter values that can be

used for pre-processing of ECG signals in order to

estimate the valuable information that can be further

used for blood pressure classiﬁcation. One parameter

is related to the signal length used for data segmen-

tation of the raw ECG signals and the other is related

to the cut-off frequency that is applied on each seg-

BIOSIGNALS 2019 - 12th International Conference on Bio-inspired Systems and Signal Processing

Table 4: Performance results for best meta-model trained for each combination.

Comb. ACC KAPPA PRC ROC F COR PR RC RAE RRSE RMSE INF MAR MF LL MI PRS Ranking

10/0.05 55.16 0.33 0.54 0.71 0.53 0.32 0.54 0.55 68.23 108.14 0.51 0.31 0.37 0.55 56.59 0.18 96.75 14

10/0.10 46.99 0.24 0.53 0.68 0.45 0.25 0.54 0.47 77.09 109.25 0.54 0.27 0.29 0.47 32.67 0.12 58.12 27

10/0.15 55.30 0.30 0.53 0.70 0.55 0.33 0.56 0.55 66.71 104.72 0.50 0.32 0.34 0.55 40.72 0.19 61.14 16

10/0.20 51.46 0.22 0.51 0.67 0.49 0.23 0.50 0.51 73.99 110.49 0.52 0.24 0.28 0.51 19.38 0.09 29.71 28

10/0.25 54.01 0.31 0.52 0.69 0.54 0.31 0.55 0.54 71.47 110.71 0.52 0.32 0.30 0.54 33.26 0.14 57.64 22

10/0.30 63.14 0.44 0.62 0.78 0.64 0.47 0.68 0.63 58.84 95.76 0.46 0.47 0.45 0.63 52.12 0.22 93.10 1

10/0.35 56.38 0.28 0.60 0.72 0.56 0.32 0.56 0.56 67.75 101.45 0.49 0.32 0.32 0.56 25.18 0.13 40.82 18

10/0.40 50.19 0.24 0.48 0.64 0.50 0.24 0.51 0.50 80.58 116.81 0.55 0.23 0.27 0.50 26.72 0.10 45.47 30

10/0.45 52.68 0.30 0.48 0.66 0.51 0.28 0.52 0.53 75.96 112.93 0.53 0.27 0.34 0.53 32.58 0.15 54.57 24

10/0.50 52.68 0.29 0.49 0.67 0.50 0.28 0.50 0.53 73.16 112.33 0.53 0.27 0.34 0.53 29.21 0.13 49.54 25

20/0.05 56.88 0.35 0.55 0.73 0.54 0.34 0.53 0.57 63.21 106.86 0.51 0.33 0.42 0.57 45.39 0.21 73.83 11

20/0.10 56.07 0.34 0.55 0.72 0.52 0.33 0.53 0.56 69.25 106.40 0.50 0.33 0.42 0.56 48.09 0.20 77.33 12

20/0.15 57.24 0.24 0.57 0.65 0.57 0.23 0.58 0.57 66.19 103.87 0.50 0.23 0.24 0.57 11.33 0.07 20.14 23

20/0.20 57.14 0.32 0.55 0.70 0.57 0.33 0.57 0.57 67.37 104.82 0.50 0.34 0.34 0.57 29.84 0.15 47.90 15

20/0.25 51.64 0.25 0.51 0.66 0.51 0.25 0.52 0.52 75.33 109.22 0.51 0.26 0.23 0.52 26.49 0.11 44.91 26

20/0.30 55.56 0.28 0.58 0.70 0.56 0.30 0.57 0.56 66.80 101.72 0.48 0.30 0.29 0.56 12.89 0.08 20.42 21

20/0.35 65.56 0.37 0.63 0.73 0.65 0.39 0.65 0.66 56.85 94.44 0.46 0.39 0.39 0.66 19.70 0.13 34.84 5

20/0.40 51.03 0.24 0.45 0.61 0.51 0.23 0.51 0.51 78.26 115.49 0.55 0.24 0.22 0.51 39.74 0.10 68.30 29

20/0.45 61.03 0.41 0.58 0.76 0.59 0.41 0.59 0.61 65.11 93.24 0.44 0.41 0.45 0.61 53.41 0.25 86.30 2

20/0.50 54.87 0.33 0.51 0.67 0.55 0.33 0.56 0.55 77.03 103.46 0.49 0.34 0.35 0.55 35.22 0.16 60.30 20

30/0.05 58.64 0.36 0.58 0.75 0.57 0.37 0.56 0.59 61.19 101.95 0.49 0.36 0.40 0.59 70.00 0.24 115.57 7

30/0.10 54.15 0.32 0.53 0.68 0.55 0.34 0.59 0.54 72.85 109.31 0.52 0.35 0.30 0.54 68.93 0.23 115.65 13

30/0.15 60.96 0.41 0.60 0.75 0.62 0.43 0.64 0.61 62.35 97.53 0.47 0.43 0.42 0.61 45.74 0.20 80.81 3

30/0.20 58.48 0.38 0.53 0.69 0.57 0.37 0.58 0.58 67.72 105.90 0.50 0.37 0.41 0.58 47.47 0.21 77.22 10

30/0.25 52.73 0.30 0.54 0.69 0.52 0.34 0.59 0.53 74.38 108.59 0.52 0.36 0.32 0.53 52.02 0.19 88.54 19

30/0.30 56.07 0.33 0.52 0.68 0.56 0.33 0.56 0.56 70.54 105.38 0.50 0.32 0.33 0.56 43.67 0.16 79.52 17

30/0.35 58.37 0.37 0.58 0.74 0.57 0.37 0.57 0.58 64.60 101.66 0.48 0.37 0.40 0.58 49.26 0.22 79.46 9

30/0.40 62.73 0.36 0.59 0.74 0.60 0.43 0.65 0.63 58.09 100.81 0.48 0.48 0.42 0.63 27.00 0.25 40.09 4

30/0.45 59.41 0.38 0.59 0.73 0.59 0.38 0.59 0.59 68.63 100.51 0.47 0.39 0.38 0.59 45.21 0.15 88.38 8

30/0.50 60.48 0.40 0.59 0.74 0.60 0.41 0.60 0.60 68.74 95.42 0.45 0.41 0.41 0.60 37.36 0.18 67.44 6

Table 5: Averaged performance results from all meta-models trained for each combination.

Comb. ACC KAPPA PRC ROC F COR PR RC RAE RRSE RMSE INF MAR MF LL MI PRS Ranking

10/0.05 44.32 0.14 0.45 0.60 0.43 0.14 0.46 0.44 83.80 117.68 0.56 0.14 0.16 0.44 14.64 0.06 24.40 16

10/0.10 43.05 0.12 0.46 0.58 0.43 0.13 0.48 0.43 85.39 119.59 0.57 0.14 0.12 0.43 15.59 0.06 26.19 21

10/0.15 44.02 0.13 0.45 0.59 0.43 0.13 0.46 0.44 84.55 119.00 0.57 0.14 0.14 0.44 14.43 0.06 23.65 19

10/0.20 44.28 0.15 0.44 0.59 0.43 0.15 0.47 0.44 85.16 118.83 0.57 0.16 0.16 0.44 18.22 0.07 30.27 14

10/0.25 45.89 0.15 0.47 0.60 0.45 0.15 0.48 0.46 81.93 116.21 0.55 0.16 0.15 0.46 14.88 0.06 24.46 12

10/0.30 43.78 0.14 0.46 0.60 0.43 0.14 0.47 0.44 83.49 117.40 0.56 0.15 0.14 0.44 15.84 0.06 26.07 15

10/0.35 44.07 0.13 0.46 0.59 0.43 0.14 0.46 0.44 84.54 117.58 0.56 0.13 0.14 0.44 11.94 0.05 18.98 18

10/0.40 42.43 0.11 0.43 0.58 0.41 0.11 0.44 0.42 86.35 120.03 0.57 0.11 0.11 0.42 12.52 0.04 21.03 26

10/0.45 44.51 0.13 0.46 0.59 0.43 0.13 0.45 0.45 83.31 116.29 0.55 0.13 0.15 0.45 10.94 0.05 17.06 17

10/0.50 41.80 0.10 0.44 0.58 0.40 0.10 0.43 0.42 86.91 118.43 0.57 0.10 0.12 0.42 10.41 0.04 16.55 29

20/0.05 42.80 0.13 0.45 0.59 0.42 0.13 0.46 0.43 85.39 118.85 0.57 0.13 0.15 0.43 16.57 0.06 28.14 20

20/0.10 46.08 0.16 0.46 0.59 0.45 0.16 0.49 0.46 81.75 117.28 0.56 0.17 0.17 0.46 18.36 0.06 31.33 11

20/0.15 41.65 0.10 0.44 0.57 0.41 0.11 0.45 0.42 87.05 121.09 0.58 0.12 0.11 0.42 11.59 0.05 18.65 28

20/0.20 42.90 0.12 0.45 0.58 0.42 0.13 0.47 0.43 86.06 118.77 0.57 0.13 0.13 0.43 15.25 0.06 25.31 22

20/0.25 41.97 0.09 0.44 0.57 0.41 0.10 0.45 0.42 86.71 120.46 0.58 0.10 0.10 0.42 11.93 0.05 19.48 30

20/0.30 42.65 0.12 0.44 0.57 0.41 0.11 0.44 0.43 86.93 119.17 0.57 0.12 0.13 0.43 14.81 0.05 24.20 23

20/0.35 41.61 0.10 0.44 0.57 0.40 0.10 0.44 0.42 87.53 120.54 0.58 0.10 0.11 0.42 14.66 0.06 23.60 27

20/0.40 41.76 0.11 0.44 0.57 0.40 0.11 0.45 0.42 87.62 120.57 0.58 0.12 0.12 0.42 15.25 0.06 23.65 25

20/0.45 44.69 0.13 0.47 0.60 0.44 0.14 0.47 0.45 83.58 116.39 0.56 0.15 0.14 0.45 15.72 0.07 24.58 13

20/0.50 42.97 0.10 0.45 0.58 0.42 0.11 0.44 0.43 86.02 118.99 0.57 0.11 0.11 0.43 11.88 0.05 18.69 24

30/0.05 47.96 0.19 0.49 0.62 0.47 0.19 0.50 0.48 78.77 115.69 0.55 0.20 0.21 0.48 23.61 0.09 40.29 5

30/0.10 49.21 0.19 0.49 0.62 0.49 0.20 0.52 0.49 78.07 114.34 0.54 0.21 0.20 0.49 21.45 0.08 36.08 1

30/0.15 47.88 0.20 0.49 0.63 0.47 0.21 0.52 0.48 78.95 114.58 0.55 0.22 0.21 0.48 21.63 0.09 37.32 2

30/0.20 48.21 0.20 0.48 0.62 0.47 0.21 0.51 0.48 79.81 115.27 0.55 0.22 0.22 0.48 24.05 0.10 40.22 3

30/0.25 46.82 0.18 0.48 0.62 0.46 0.18 0.49 0.47 80.50 115.71 0.55 0.19 0.19 0.47 24.06 0.09 40.49 7

30/0.30 45.69 0.16 0.46 0.61 0.44 0.16 0.48 0.46 82.11 116.52 0.56 0.17 0.17 0.46 19.97 0.08 33.25 10

30/0.35 47.53 0.16 0.48 0.60 0.46 0.17 0.50 0.48 79.27 114.75 0.55 0.18 0.16 0.48 19.43 0.08 32.22 8

30/0.40 48.41 0.18 0.48 0.60 0.47 0.18 0.50 0.48 78.94 114.78 0.55 0.19 0.19 0.48 19.29 0.07 32.74 6

30/0.45 48.31 0.18 0.47 0.62 0.47 0.19 0.49 0.48 79.17 114.26 0.54 0.19 0.19 0.48 25.97 0.10 43.66 4

30/0.50 45.79 0.18 0.48 0.63 0.44 0.19 0.50 0.46 82.13 115.70 0.56 0.20 0.21 0.46 19.59 0.07 32.44 9

ment for baseline removal. For this reason, 30 com-

binations were experimentally evaluated; each one is

a combination of different signal length used for data

segmentation and cut-off frequency for baseline re-

moval. For the signal lengths: 10 s, 20 s, and 30 s,

were used; while the cut-off frequency starts from

0.05 Hz, till 0.50 Hz, with a step of 0.05 Hz.

The evaluation of these combinations was per-

formed in a combination with complexity analysis

used for feature extraction that are further used for

blood pressure classiﬁcation. The evaluation was

done using a dataset that contains data from ﬁve com-

mercially available ECG sensors. The dataset was

pre-processed for each combination separately. The

same classiﬁcation methodology was used for each

pre-processed dataset, which results are only used to

estimate the valuable information that these combina-

tions convey. A data-driven methodology from multi-

criteria decision analysis was used to rank these com-

binations regarding a set of 17 performance measures.

Evaluation results showed that the best results can

be achieved using a signal length of 30 s, so it fol-

lows that it carries the most valuable information. Re-

garding the cut-off frequency, it follows that good

models are achieved when it is between 0.10 Hz and

0.20 Hz. This is a contribution to the arguments pub-

Comparing Different Settings of Parameters Needed for Pre-processing of ECG Signals used for Blood Pressure Classiﬁcation

lished in the literature discussing the optimal ECG

sample lengths needed for building predictive models

(Takahashi et al., 2017; Shdefat et al., 2018), as well

as the lower frequencies where the ECG components

overlap with the baseline wander noise (Xu et al.,

2017). Also, this analysis can further give additional

directions for research, where the pre-processing will

not be made regarding a ﬁx signal length and cut-off

frequency for baseline removal, but it can produce in-

formation that can be used for information fusion ap-

proaches.

ACKNOWLEDGEMENTS

This work was supported by the Slovenian Research

Agency (research core funding No. P2-0098, and

project J1-8155), and by SIARS, NATO multi-year

project NATO.EAP.SFPP 984753.

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BIOSIGNALS 2019 - 12th International Conference on Bio-inspired Systems and Signal Processing