A Novel Approach for Modelling the Relationship between Blood

Pressure and ECG by using Time-series Feature Extraction

Stefan Kochev

1 a

, Neven Stevchev

2 b

, Svetlana Kocheva

3 c

, Tome Eftimov

4 d

and Monika Simjanoska

1 e

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

Rugjer Boshkovikj 16, 1000 Skopje, North Macedonia

PZU d-r Andon Kochev, Javor bb, Radovish, North Macedonia

Medical Faculty, Ss. Cyril and Methodius University, 50th Division 6, 1000 Skopje, North Macedonia

Computer Systems Department, Jo

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

Keywords:

ECG, Blood Pressure, Machine Learning, Feature Extraction, Time-series Analysis.

Abstract:

This paper addresses the ECG-blood pressure relationship - a fact that physicians have discussed for years.

The hypothesis set in the paper is that blood pressure is related to electrocardiogram (ECG) and that the

systolic blood pressure (SBP) and diastolic blood pressure (DBP) values can be predicted by using information

only from a given ECG signal. Therefore, we established a protocol for creating a database considering

measurements from real patients in ambulance environment, and consequently developed methodology for

analysing the collected measurements. The proposed methodology follows two steps: i) ﬁrst the signals are

considered as time series data, and ii) a time series feature extraction method is applied to extract the important

features from the ECG signals. Hereafter, a novel Machine learning method is applied (CLUS) that produced

best results among the traditionally-used Machine learning methods. The best results obtained are 12.81 ±

2.66 mmHg for SBP and 8.12 ± 1.80 mmHg for DBP. After introducing calibration method the obtained mean

absolute errors (MAEs) reduced to 6.93 ± 4.70 mmHg for SBP, and 7.13 ± 4.48 mmHg for DBP. Given the

latest literature, the results are appropriately compared and conﬁrm the relation between the ECG signal and

the blood pressure.

1 INTRODUCTION

Cardiovascular diseases (CVDs) are among the top

ﬁve causes of death worldwide according to World

Health Organization research (Organization et al.,

2016). Ischemic heart disease and stroke are at the

top of this list. This fact indicates the particular im-

portance of prevention as well as early diagnosis and

treatment of CVDs. Blood pressure (BP) is one of the

vital signs used to diagnose these diseases. Regular

BP monitoring is especially important to detect pos-

sible changes in the functioning of the cardiovascular

system.

Recently, there is an expansion of wearable de-

https://orcid.org/0000-0001-9889-2286

https://orcid.org/0000-0003-3034-2995

https://orcid.org/0000-0002-5627-7899

https://orcid.org/0000-0001-7330-1902

https://orcid.org/0000-0002-5028-3841

vices equipped with biosensors designed to monitor

the activity of the human body by measuring some

of the vital signs. They come in the form of smart

watches, smart hand bracelets, belts, rings, smart

glasses, etc. Usually, smart watches are equipped

with motion detection sensors and Photoplethysmog-

raphy (PPG) sensors. Detailed analysis of these

measurements can provide important information to

physicians about the functioning of the cardiovascular

system of the patients. Even though the smart watches

and the wearable sensors are still not equipped with

BP sensors, the newest generation of smart watches

provides dual-electrode ECG sensors, e.g. the famous

Apple Watch in the latest two versions (fourth and

ﬁfth), is equipped with ECG functionality (Hernando

et al., 2018). This is very important for the research

presented in this paper, since the main goal is to lever-

age the ECG signal in order to estimate the BP condi-

tion of the patient.

228

Kochev, S., Stevchev, N., Kocheva, S., Eftimov, T. and Simjanoska, M.

A Novel Approach for Modelling the Relationship between Blood Pressure and ECG by using Time-series Feature Extraction.

DOI: 10.5220/0008967202280235

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS, pages 228-235

ISBN: 978-989-758-398-8; ISSN: 2184-4305

Several studies have been performed on the re-

lationship between blood pressure and morphologi-

cal characteristics of the ECG, which conﬁrm that

there is no strong relationship between the morpho-

logical characteristics of the signal and hypertension

(Schroeder et al., 2003; Hassan et al., 2008). How-

ever, in the most recent research (Mousavi et al.,

2019a), this hypothesis is rejected since the results

clearly show ECG-BP relation based on the morpho-

logical characteristics of the ECG signal.

In this paper, a new approach for exploring the

ECG-BP relationship is proposed. The core of the

new approach is in the feature engineering phase. The

ECG signal is considered as time series data and Ma-

chine Learning models have been trained, which use

a space of 794 discriminatory time series features.

The paper is organized as follows. Section 2

presents the most reliable work related to the research

presented in this paper. The related work is orga-

nized in a table, which also encompasses our results,

to make it easily comparable with the literature. Since

we follow the regular procedure for biomedical signal

processing proposed in (Najarian and Splinter, 2005),

the ﬁrst step to describe the biological system of inter-

est in the paper is described in Section 3. The sensors

used, the database, the preprocessing, the features se-

lection, and the complete Machine learning approach

are described in Section 4. Section 5 presents the re-

sults and the discussion, whereas the ﬁnal conclusions

are presented in Section 6.

2 RELATED WORK

More attempts have been made to establish the rela-

tionship between BP and some parameters that can be

measured with wearable devices, such as PPG sensor.

However, the majority of them have used a combina-

tion of multiple sensors and vital signs information,

which requires an installation of additional measure-

ment devices. Only a few papers are studying the re-

lationship between the BP and the ECG only.

Given the published results in the literature, in Ta-

ble 1 we present many of the achievements evaluated

in the literature by the mean absolute error and stan-

dard deviation, MAE ± SD. The second cluster of re-

sults in the same table relate to the latest research pub-

lished in 2018 and 2019. In the third cluster, found in

the table, the results published in this paper are iso-

lated so that they can be easily compared with the re-

sults published in the literature.

From the related literature, we have identiﬁed only

four other studies in which ECG has been used to as-

sess BP. However, the methods in two of the studies

use an additional sensor (PPG sensor).

All the errors are measured in mmHg unit.

3 THE BIOLOGICAL SYSTEM

Electrocardiography is a standard method of record-

ing the impulses of electrical activity of the heart mus-

cle. The graphical representation of the heart’s elec-

trical bio-potentials is called an electrocardiogram

(ECG). Deviations from the normal ECG shape occur

at numerous cardiac abnormalities, including changes

in rhythm (such as atrial ﬁbrillation and ventricular

Table 1: Related work comparison.

Research Source Num. sub-

jects

Age Records Method MAE SBP MAE DBP MAE MAP

(Gao et al., 2016) PPG 65 22–65 78 Wavelet, SVM 5.1 ± 4.3 4.6 ± 4.3 N/A

(Ahmad et al., 2012) ECG, PTT 10 24–63 150 Numerical solution ±5.93 ±4.76 ±4.23

(Chen et al., 2013) BCG, ECG 5 / / Analytical solution 9 ± 5.6 1.8 ± 1.3 N/A

(Daimiwal et al., 2014) PPG 16 18–48 / Frequency analysis 0.8 ± 7 0.9 ± 6 N/A

(Chan et al., 2001) ECG, PPG, PAT / / / Analytical solution 7.49 ± 8.8 4.07 ± 5.6 N/A

(Kachuee et al., 2015) PPG MIMIC II

(Saeed et al.,

2011)

adults 4254 Linear regression,

SVM

13.84 ± 17.56 6.96 ± 9.16 8.54 ± 10.87

(Yamanaka et al., 2016) PTT 127 / / Wavelet ±7.63 H/B H/B

(Ding et al., 2016) PTT, PPG 27 21–29 / Analytical solution −0.37 ± 5.21 −0.08 ± 4.06 −0.18 ± 4.13

(Su et al., 2018) ECG + PCG 84 / / Deep RNN [3.84-5.81] (RMSE) [1.80-5.21] (RMSE)

(Katayama et al., 2018) FBG sensor 77 21-87 132 PLSR, ANN Unknown: 12±17

(Zakrzewski and Anthony, 2018) Ultrasound (images) 24 / / Regression, Bland-

Altman

10.21 8.23

(Mousavi et al., 2019b) PCG 400 1323 DT,SVR, ABR,RFR [4.17-7.51] (SD) [8.90-18.54] (SD)

(Wu et al., 2018) ECG + PCG 85 / / DNN 3.63 2.45

(Fan et al., 2018) PCG 6 20-35 / Gaussian model 8.42±8.81 12.34±7.10

(Mousavi et al., 2019a) ECG 7 50-81 7 morphological fea-

tures, McSharry’s

method

1.125 ± 3.125 N/A N/A

(Yang et al., 2018) ECG, PPG 14 N/A N/A Lasso regression,

SVM

12.38 ± 16.17 6.34 ± 8.45 N/A

(Simjanoska et al., 2018) ECG 72 16 – 83 1253 Complexity analysis 7.93 ± 8.16 6.41 ± 7.5 5.72 ± 6.69

Our approach ECG 69 18 - 89 2073 tsfresh + CLUS 12.81 ± 2.66 8.12 ± 1.80 N/A

Our approach (calibrated) ECG 69 18 - 89 2073 tsfresh + CLUS 6.93 ± 4.70 7.13 ± 4.48 N/A

A Novel Approach for Modelling the Relationship between Blood Pressure and ECG by using Time-series Feature Extraction

229

tachycardia), inadequate blood ﬂow to the coronary

artery, and electrolyte disturbances.

Blood pressure is deﬁned as the force by which

blood presses on the walls of the arteries at different

stages of a cardiac cycle, and is measured in millime-

ters of mercury (mmHg). Usually BP is presented as

two values, that is, systolic and diastolic blood pres-

sure. Systolic blood pressure (SBP) is the pressure at

which the blood presses on the walls of the arteries

when the heart is pumped, i.e., the highest pressure in

a heart cycle (known as a pumping phase). Diastolic

blood pressure (DBP) is the pressure on the walls of

the arteries in the phase of ﬁlling the heart with blood,

that is, when the heart rests between two beats (or the

lowest blood pressure between two beats). Usually

greater attention is given to SBP as a major factor

for CVDs. According to recent studies, the risk of

death from ischemic heart disease and stroke is dou-

bled with every rise of 20 mmHg of systolic and 10

mmHg of DBP for people from 40 to 89 years (Asso-

ciation et al., 2014).

4 METHODOLOGY

To develop models that can be used for predicting

blood pressure using ECG time series, we propose our

own methodology that consists of four main steps:

• Data collection;

• Preprocessing of ECG signals;

• Training regression models for blood pressure

prediction;

• Methodology evaluation.

The methodology ﬂowchart is presented in Figure 1.

Further, each step is described in detail.

4.1 Data Collection

The data collected in our study consists of 69 partic-

ipants, who agreed to be a part of the study. For the

data collection process, we established a protocol for

creating a data set that considers patients’ measure-

ments obtained in an ambulance environment.

The reason for creating the de novo database, in-

stead of using publicly available resources as Phys-

ionet’s Charis database, is the fact that the partici-

pants involved in the Charis database suffered trau-

matic brain injuries (Kim et al., 2016). Considering

such brain injuries, all of those patients would exhibit

a special case of hypertension, referred to as isolated

systolic hypertension, thus obtaining SBP of ≥ 140

and DBP of < 90 (Simjanoska et al., 2018). This

makes Charis database unacceptable for our study

since we would lack patients with normal blood pres-

sure values. In addition to this reason is the fact that

we want to prove the usefulness of the biosensors

technology in a case when there is a lack of medical

equipment.

Before the ECG signal and BP measurements

were collected, we obtained a study approval from the

Ethics committee, which is a part from the Faculty of

Medicine within “Ss. Cyril and Methodius” Univer-

sity in Skopje. Additionally, each participant signed

an agreement that their information can be used for

the purpose of the research study. The participants

data is anonymized.

The measurements were collected in a clinical

conditions with a help of physician. The physician

helped preparing the participant before measurements

start, by setting participants in the appropriate seating

position, way of breathing and their posture. After

that, the ECG signal is measured for three minutes

using a commercially available ECG device, known

as Zephyr ECG (Zephyr Technology, 2017). The

reliability of the device is previously conﬁrmed in

other studies (Hailstone and Kilding, 2011). Addi-

tionally, at the beginning and at the end of the ECG

measurement, the physician measured the SBP and

DBP for every participant by using a standardized de-

vice for blood pressure (i.e. the traditional cuff-based

method). The data is taken into consideration only if

the two (consecutive) pairs of BP measurements are

in the range of ± 5 mmHg. The average of the two

pairs is taken as a ﬁnal BP reading, assigned to the 3

minutes long ECG.

4.2 ECG Signals Preprocessing

After the data is collected, the next step is to prepro-

cess the data in order to create a training corpus that

will be used to train regression models for BP predic-

tion. The preprocessing step consists of several sub-

steps:

• Segmentation;

• Noise removal;

• Feature engineering.

4.2.1 Segmentation

Since we are working with ECG signals that are time

series from three minutes, we segmented each signal

into segments with a signal length of 10 s. Addition-

ally, to each segment we assigned the SBP and DBP

real values from the whole ECG signal.

BIOSIGNALS 2020 - 13th International Conference on Bio-inspired Systems and Signal Processing

230

Data

collection

Ethics committee

Research

approval

Mobile

Application

Development

Zephyr

Sense

Android

Application

Zephyr ECG

sensor

Expert

Participants

Cuff-based blood

pressure monitor

ECG signal samples

+ blood pressure

dataset

Preprocessing

Segmentation

...

10 seconds

Noise removal

Feature

engineering

1.15, 0.13, ...

2.19, 1.23, ...

4.12, 5.78, ...

0.76, 4.43, ...

...

Feature

vectors

Training

dataset

Regression

models training

1.15, 0.13, 0.33, 0.55, 0.76, ...

2.19, 1.23, 0.05, 0.88, 0.93, ...

4.12, 5.78, 0.21, 0.78, 0.70, ...

0.76, 4.43, 0.22, 0.21, 0.98, ...

4.12, 5.78, 0.21, 0.78, 0.70, ...

...

Feature selection

Hyperparameter tuning

and evaluation

Repeat and select

the best model

Methodology

evaluation

Training a regression

model

120

Regression model

Final regression models

Model callibration

Figure 1: Methodology ﬂowchart.

4.2.2 Noise Removal

When measuring ECG signals, the signal obtained

contains a noise, that can be caused either by the de-

vice, or the environment where the measurements are

performed. For this reason, bandpass ﬁlters can be

used for noise removal. In this research, Butterworth

bandpass ﬁlter is selected that can be used with cut-off

frequency between 0.05 Hz and 100 Hz. The selec-

tion of the frequencies is supported by the results pub-

lished in (Simjanoska et al., 2018; Simjanoska et al.,

2019).

4.2.3 Feature Engineering

In order to extract useful information from the ECG

signal, we have come up with a new form of repre-

senting the ECG signal data in terms of time-series

features.

Manual deﬁnition and computation of an ECG

time series features is a time-consuming process

which requires an expert knowledge. An approach

of manual deﬁnition and computation of ECG fea-

tures has been already studied in a previous research

(Simjanoska et al., 2018; Simjanoska et al., 2019).

The new approach proposed in this paper deals with

automation of the feature engineering step, utilizing

the tsfresh library for ECG feature extraction (Christ

et al., 2018).

TSFresh is a python library that deals with au-

tomatic feature extraction for representing a time se-

ries data. Utilizing this library, 794 features can be

extracted for every 10s-long segment. There are 65

unique features, described in the documentation of the

tsfresh library (TSFresh, 2016). The number of 794

features for every segment is a result of computing

many of these features with different parameter set-

tings. Some of the features computed by the tsfresh

library are: absolute energy, absolute sum of changes,

autocorrelation, entropy, binned entropy, number of

values above and below the average, friedrich coefﬁ-

cients, skewness, symmetry looking, etc.

4.3 Regression Models Creation

The next challenge was creating regression models

that will be able to predict the real SBP and DBP val-

ues. For this reason, the training corpus that is de-

scribed by the tsfresh features is used again. In order

to shrink the feature space consisting of 794 features,

feature selection techniques are applied. Two differ-

ent approaches are used to train regression models.

In the ﬁrst scenario, two different single linear regres-

sion models are trained (one for SBP and the other for

DBP). In this scenario, the SBP and DBP values are

assumed to be independent. To create the models we

evaluate Random Forest (Liaw et al., 2002), Gradient

Boosting (Friedman, 2002), Bagging (Sutton, 2005),

and XGBoost (Chen et al., 2015) methods. In the sec-

ond scenario, one multi-target linear regression model

is trained by using Clus Random Forest (Kocev et al.,

2013). In this scenario, the SBP and DBP values are

assumed to be dependent.

A Novel Approach for Modelling the Relationship between Blood Pressure and ECG by using Time-series Feature Extraction

231

4.3.1 Calibration

Since we are working with sensors that measure vital

signs and also moving in the direction of personalized

medicine, in most cases we should adapt the models

with regard to the user. In our approach, we propose

a calibration which includes several user’s segments

in the training set used to train the regression mod-

els. This is motivated from the reason that on that

way the model can learn the speciﬁc relations that ex-

ist between the ECG and BP for the speciﬁc user. It

can be used as online learning approach, where at the

beginning, a pretrained model is provided, and after

receiving several segments from the user, the model

will start to adapt.

4.4 Methodology Evaluation

In order to evaluate the regression models, and

eliminate the possibility of overﬁtting, a Leave-one-

participant-out validation is used. Again, all the seg-

ments from one participant are used for testing, and

the segments from the other participants are used to

train a model. After applying the trained model, mean

absolute error (MAE) is calculated for each segment

from the testing participant. Eventually, the average

MAE is calculated considering all the segments from

the testing participant. In addition to MAE, the stan-

dard deviation (SD) is also calculated. This process

is repeated for every participant, and ﬁnally, the aver-

age MAE and SD from the values associated to every

participant, are reported.

5 RESULTS AND DISCUSSION

In this section we describe the data set used for our

experiments, as well as the obtained results from the

regression models, followed by a discussion.

5.1 Data

The data set consists of ECG signals and BP measure-

ments for each of the 69 participants, including 43

men and 26 women of age in range 18-89. The ECG

signal is acquired using the Zephyr ECG device. Sys-

tolic and diastolic BP readings, acquired with a stan-

dard cuff-based BP device, are assigned to each ECG

signal.

For 55 of the participants, an ECG signal of 3 min-

utes length is measured. The sampling frequency of

the Zephyr ECG device is 250Hz, providing a signal

of 45,000 points in length (3min x 60sec x 250Hz =

45,000 ECG signal points). For 5 of the participants,

an ECG of different length is measured. For the rest of

9 participants, several distinct ECG signals of differ-

ent length, accompanied with BP readings, have been

acquired.

The segmentation step results in total of 2073 seg-

ments with a signal length of 10 seconds. This in-

cludes 990 ECG segments for 55 of the participants

with 3 minutes long ECG signal and 1083 segments

for the rest of 14 participants for which several ECG

signals of different length have been acquired.

5.2 Feature Selection

The “curse of dimensionality” is a problem that

typically arises when analyzing a data in high-

dimensional feature space. The feature space of 794

attributes is a high-dimensional space, and as it turns

out, many of these features are correlated. To reduce

the number of features, correlation analysis is per-

formed (Ji et al., 2001), followed by removing fea-

tures with a Pearson’s correlation greater than 0.75.

After that, a model-based feature selection is used, re-

moving the least signiﬁcant features, with respect to

the feature weights computed by the Gradient Boost-

ing model (Xu et al., 2014). It is important to be

mentioned that the ﬁnal set of features depends on

the training data used in each iteration (according to

the leave-one-participant out rules), since the train-

ing data is used for the correlation analysis and the

model-based selection of features.

In most cases, the combination of these two ap-

proaches results in a feature space of around 450 fea-

tures and better regression models as explained in the

following steps.

5.3 Regression

To predict the SBP and DBP value, ﬁve different re-

gression algorithms are applied: i) Random Forest, ii)

Gradient Boosting, iii) Bagging Regressor, iv) XG-

Boost Regressor, and v) CLUS. As evaluation metric

mean absolute error (MAE) is selected together with

the standard deviation (SD) averaged across all pa-

tients, as reported in Table 2.

Using the table, it can be seen that CLUS provided

most promising results that are also in the range of

state-of-the-art approaches used for the same prob-

lem. In this case, only one multi-target regression

model is trained. It achieved an average error of 12.81

± 2.66 mmHg for the SBP and 8.12 ± 1.80 mmHg for

the DBP. The other models that learn separate linear

regression models for SBP and DBP, and therefore,

treating them as two independent problems, provide

larger errors.

BIOSIGNALS 2020 - 13th International Conference on Bio-inspired Systems and Signal Processing

232

5.3.1 Calibration

The proposed calibration method can be evaluated

with nine of the participants, as only for these par-

ticipants several distinct measurements (ECG signal

+ BP readings) have been taken. The distinct mea-

surements for one participant are taken during several

days. The BP values vary for each of the measure-

ments. To show the calibration process, in Table 3,

we presented the calibration results for these nine par-

ticipants. In that case, one participant’s measurement

is included in the training phase, while the testing is

done on the other participant’s measurements.

Considering the calibration results provided in the

table, it can be seen that it really helps the model to

adapt the relations between the ECG signal and BP of

a speciﬁc user, which results in smaller errors, even

though only one participant’s measurement is used in

the training set. Taking into account the results in Ta-

ble 2, it can be concluded that the model performs

very well even without calibration applied, meaning

at most of the cases (except for patient 5 at the SBP

case, and patients 6 and 7 at DBP case), the errors are

near or even less than those reported in the same ta-

ble. However, when the calibration is applied, those

errors are reduced and meet the expectations from the

model.

When compared to the results in the literature

where the authors used distribution-based calibration

methodology (Simjanoska et al., 2018), and obtained

MAEs of 7.72 mmHg for SBP, 9.45 mmHg for DBP

and 8.13 mmHg for MAP, our results showing MAEs

of 6.93 ± 4.70 mmHg for SBP and 7.13 ± 4.48

mmHg for DBP, indicate that the proposed methodol-

ogy is competitive to those presented in state-of-the-

art literature.

Even though the calibration can be considered as

limitation and this problem should be addressed in the

future work, however, many smart devices applica-

tions nowadays require personal information before

they can be accurately used.

6 CONCLUSION

This paper presents a novel methodology for BP pre-

diction by using information only from ECG signal.

The methodology addresses the hypothesis whether

the BP is related and can be predicted from ECG.

For the goal to accept the hypothesis, we performed

experiment in which we developed a procedure for

collecting ECG signals measured at patients in ambu-

lance environment, by using commercial and reliable

biosensor (Zephyr), as well as a certiﬁed cuff-based

device for measuring the reference BP values. The

methodology developed consists of multiple steps in-

cluding preprocessing the signals, feature extraction

and Machine learning methods to build the models for

the BP prediction given the ECG signal. The core of

importance of the proposed methodology is in the fea-

ture extraction process at which a suitable time-series

method is applied to form the reliable feature space.

The best results obtained are 12.81 ± 2.66 mmHg

for SBP and 8.12 ± 1.80 mmHg for DBP. After in-

troducing calibration method the results signiﬁcantly

improved, and the obtained MAEs reduced to 6.93

± 4.70 mmHg for SBP, and 7.13 ± 4.48 mmHg for

DBP. Given the latest literature, the results obtained

from the proposed methodology conﬁrm the relation

between the ECG signal and the blood pressure.

Table 2: Regression models evaluation metrics.

Algorithm Systolic MAE Systolic SD Diastolic MAE Diastolic SD

1 Random Forest 13.99 4.13 8.65 2.71

2 Gradient Boosting 13.68 3.88 8.97 2.48

3 Bagging Regressor 13.62 3.86 8.87 2.91

4 XGBoost Regressor 13.71 3.81 8.95 2.47

5 CLUS 12.81 2.66 8.12 1.80

Table 3: Calibration evaluation on distinct testing set.

part. ID Systolic MAE Diastolic MAE Calibrated Systolic MAE Calibrated Diastolic MAE

1 7.51 ±5.25 6.35 ±4.82 5.86 ±3.62 4.05 ±4.81

2 8.71 ±4.87 8.11 ±5.63 5.72 ±3.93 5.88 ±4.61

3 4.05 ±3.54 8.11 ±3.92 3.81 ±3.53 7.55 ±3.97

4 11.64 ±6.37 6.96 ±4.35 11.25 ±6.21 6.84 ±4.34

5 13.72 ±6.65 6.55 ±4.12 7.22 ±4.59 6.40 ±4.01

6 6.31 ±3.16 21.82 ±6.31 6.15 ±4.04 17.41 ±7.36

7 7.97 ±3.54 17.11 ±4.83 6.77 ±3.66 5.61 ±2.72

8 12.02 ±7.62 6.26 ±6.84 10.14 ±6.32 5.44 ±5.09

9 6.69 ±6.70 6.93 ±4.06 5.44 ±6.39 5.02 ±3.12

A Novel Approach for Modelling the Relationship between Blood Pressure and ECG by using Time-series Feature Extraction

233

ACKNOWLEDGEMENTS

We would like to thank all participants that contribute

to the study. We would also like to thank the medical

staff employed at PZU d-r Andon Kochev, Javor bb,

Radovish, North Macedonia.

This work was supported by the Slovenian Re-

search Agency Program P2-0098 and has received

funding from the European Union’s Horizon 2020 re-

search and innovation programme under grant agree-

ment No 863059 and No 769661.

Information and the views set out in this publica-

tion are those of the authors and do not necessarily re-

ﬂect the ofﬁcial opinion of the European Union. Nei-

ther the European Union institutions and bodies nor

any person acting on their behalf may be held respon-

sible for the use that may be made of the information

contained herein.

REFERENCES

Ahmad, S., Chen, S., Soueidan, K., Batkin, I., Bolic, M.,

Dajani, H., and Groza, V. (2012). Electrocardiogram-

assisted blood pressure estimation. IEEE Transactions

on Biomedical Engineering, 59(3):608–618.

Association, A. H. et al. (2014). Understanding blood pres-

sure readings. Updated November, 17.

Chan, K., Hung, K., and Zhang, Y. (2001). Noninva-

sive and cufﬂess measurements of blood pressure for

telemedicine. In Engineering in Medicine and Biol-

ogy Society, 2001. Proceedings of the 23rd Annual In-

ternational Conference of the IEEE, volume 4, pages

3592–3593. IEEE.

Chen, T., He, T., Benesty, M., Khotilovich, V., and Tang,

Y. (2015). Xgboost: extreme gradient boosting. R

package version 0.4-2, pages 1–4.

Chen, Z., Yang, X., Teo, J. T., and Ng, S. H. (2013).

Noninvasive monitoring of blood pressure using op-

tical ballistocardiography and photoplethysmograph

approaches. In Engineering in Medicine and Biol-

ogy Society (EMBC), 2013 35th Annual International

Conference of the IEEE, pages 2425–2428. IEEE.

Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr, A. W.

(2018). Time series feature extraction on basis of scal-

able hypothesis tests (tsfresh–a python package). Neu-

rocomputing, 307:72–77.

Daimiwal, N., Sundhararajan, M., and Shriram, R. (2014).

Respiratory rate, heart rate and continuous measure-

ment of bp using ppg. In Communications and Signal

Processing (ICCSP), 2014 International Conference

on, pages 999–1002. IEEE.

Ding, X.-R., Zhang, Y.-T., Liu, J., Dai, W.-X., and Tsang,

H. K. (2016). Continuous cufﬂess blood pressure es-

timation using pulse transit time and photoplethysmo-

gram intensity ratio. IEEE Transactions on Biomedi-

cal Engineering, 63(5):964–972.

Fan, X., Ye, Q., Yang, X., and Choudhury, S. D. (2018).

Robust blood pressure estimation using an rgb cam-

era. Journal of Ambient Intelligence and Humanized

Computing, pages 1–8.

Friedman, J. H. (2002). Stochastic gradient boosting. Com-

putational statistics & data analysis, 38(4):367–378.

Gao, S. C., Wittek, P., Zhao, L., and Jiang, W. J. (2016).

Data-driven estimation of blood pressure using photo-

plethysmographic signals. In Engineering in Medicine

and Biology Society (EMBC), 2016 IEEE 38th An-

nual International Conference of the, pages 766–769.

IEEE.

Hailstone, J. and Kilding, A. E. (2011). Reliability and va-

lidity of the zephyr

bioharness

to measure respi-

ratory responses to exercise. Measurement in Physical

Education and Exercise Science, 15(4):293–300.

Hassan, M. K. B. A., Mashor, M., Nasir, N. M., and Mo-

hamed, S. (2008). Measuring of systolic blood pres-

sure based on heart rate. In 4th Kuala Lumpur Interna-

tional Conference on Biomedical Engineering 2008,

pages 595–598. Springer.

Hernando, D., Roca, S., Sancho, J., Alesanco,

A., and

Bail

on, R. (2018). Validation of the apple watch for

heart rate variability measurements during relax and

mental stress in healthy subjects. Sensors, 18(8):2619.

Ji, X.-j., Li, S.-z., and Li, T. (2001). Application of the

correlation analysis in feature selection. Journal of

Test and Measurement Technology, 15(1):15–18.

Kachuee, M., Kiani, M. M., Mohammadzade, H., and Sha-

bany, M. (2015). Cuff-less high-accuracy calibration-

free blood pressure estimation using pulse transit time.

In Circuits and Systems (ISCAS), 2015 IEEE Interna-

tional Symposium on, pages 1006–1009. IEEE.

Katayama, K., Ishizawa, H., Koyama, S., and Fujimoto, K.

(2018). Improvement of blood pressure prediction us-

ing artiﬁcial neural network. In 2018 IEEE Interna-

tional Symposium on Medical Measurements and Ap-

plications (MeMeA), pages 1–5. IEEE.

Kim, N., Krasner, A., Kosinski, C., Wininger, M., Qadri,

M., Kappus, Z., Danish, S., and Craelius, W. (2016).

Trending autoregulatory indices during treatment for

traumatic brain injury. Journal of clinical monitoring

and computing, 30(6):821–831.

Kocev, D., Vens, C., Struyf, J., and D

zeroski, S. (2013).

Tree ensembles for predicting structured outputs. Pat-

tern Recognition, 46(3):817–833.

Liaw, A., Wiener, M., et al. (2002). Classiﬁcation and re-

gression by randomforest. R news, 2(3):18–22.

Mousavi, S. S., Charmi, M., Firouzmand, M., Hemmati, M.,

and Moghadam, M. (2019a). A new approach based

on dynamical model of the ecg signal to blood pres-

sure estimation. In 2019 4th International Conference

on Pattern Recognition and Image Analysis (IPRIA),

pages 210–215. IEEE.

Mousavi, S. S., Firouzmand, M., Charmi, M., Hemmati,

M., Moghadam, M., and Ghorbani, Y. (2019b). Blood

pressure estimation from appropriate and inappropri-

ate ppg signals using a whole-based method. Biomed-

ical Signal Processing and Control, 47:196–206.

BIOSIGNALS 2020 - 13th International Conference on Bio-inspired Systems and Signal Processing

234

Najarian, K. and Splinter, R. (2005). Biomedical signal and

image processing. CRC press.

Organization, W. H. et al. (2016). Global status report

on noncommunicable diseases 2010. geneva: Who;

2011. World Health Organization.

Saeed, M., Villarroel, M., Reisner, A. T., Clifford, G.,

Lehman, L.-W., Moody, G., Heldt, T., Kyaw, T. H.,

Moody, B., and Mark, R. G. (2011). Multiparameter

intelligent monitoring in intensive care ii (mimic-ii):

a public-access intensive care unit database. Critical

care medicine, 39(5):952.

Schroeder, E. B., Liao, D., Chambless, L. E., Prineas,

R. J., Evans, G. W., and Heiss, G. (2003). Hyper-

tension, blood pressure, and heart rate variability: the

atherosclerosis risk in communities (aric) study. Hy-

pertension, 42(6):1106–1111.

Simjanoska, M., Gjoreski, M., Gams, M., and Made-

vska Bogdanova, A. (2018). Non-invasive blood pres-

sure estimation from ecg using machine learning tech-

niques. Sensors, 18(4):1160.

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

(2019). Comparing different settings of parameters

needed for pre-processing of ecg signals used for

blood pressure classiﬁcation. In 12th International

Conference on Bio-inspired Systems and Signal Pro-

cessing, pages 62–72.

Su, P., Ding, X.-R., Zhang, Y.-T., Liu, J., Miao, F., and

Zhao, N. (2018). Long-term blood pressure predic-

tion with deep recurrent neural networks. In 2018

IEEE EMBS International Conference on Biomedical

& Health Informatics (BHI), pages 323–328. IEEE.

Sutton, C. D. (2005). Classiﬁcation and regression trees,

bagging, and boosting. Handbook of statistics,

24:303–329.

TSFresh (2016). List of features computed by the tsfresh li-

brary. https://tsfresh.readthedocs.io/en/latest/text/list\

of\ features.html.

Wu, D., Xu, L., Zhang, R., Zhang, H., Ren, L., and Zhang,

Y.-T. (2018). Continuous cuff-less blood pressure es-

timation based on combined information using deep

learning approach. Journal of Medical Imaging and

Health Informatics, 8(6):1290–1299.

Xu, Z., Huang, G., Weinberger, K. Q., and Zheng, A. X.

(2014). Gradient boosted feature selection. In Pro-

ceedings of the 20th ACM SIGKDD international con-

ference on Knowledge discovery and data mining,

pages 522–531. ACM.

Yamanaka, S., Morikawa, K., Yamamura, O., Morita, H.,

and Huh, J. Y. (2016). The wavelet transform of

pulse wave and electrocardiogram improves accuracy

of blood pressure estimation in cufﬂess blood pressure

measurement.

Yang, S., Zaki, W., Morgan, S., Cho, S.-Y., Correia, R.,

Wen, L., and Zhang, Y. (2018). Blood pressure esti-

mation from photoplethysmogram and electrocardio-

gram signals using machine learning.

Zakrzewski, A. M. and Anthony, B. W. (2018). Noninva-

sive blood pressure estimation using ultrasound and

simple ﬁnite element models. IEEE Transactions on

Biomedical Engineering, 65(9):2011–2022.

Zephyr Technology (2017). Zephyr bioharness 3.0 user

manual. https://www.zephyranywhere.com/media/

download/bioharness3-user-manual.pdf.

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