Empirical Evaluation of the Potential of Low-cost and Open Source

“On-the-Person” ECG for Cardiopathy Pre-screening

H´elio B. M. Lourenc¸o

1,2

, V´ıctor Sanﬁns

, S´ılvia Ala

4,5,6

, Francisco Barros

2,5

, Hugo P. Silva

and Manuel J.C.S. Reis

2,8

Ace Centre, Abingdon, OX14 1RG, U.K.

University of Tr´as-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal

Hospital de Guimar˜aes, Servic¸o de Cardiologia/Laborat´orio de Arritmologia, Pacing e Electroﬁsiologia, Portugal

Instituto Polit´ecnico de Braganc¸a, Departamento de Ciˆencias Sociais e Gerontologia, Portugal

Inst. Inves. Sanitaria Galicia Sur—Grupo de Investigaci´on en Neurociencia y Enfermedades Psiqui´atricas, Spain

Neurosciences and Clinical Psychology, University of Vigo, Spain

IT—Instituto de Telecomunicac¸ ˜oes, EST/IPS—Escola Superior de Tecnologia do Instituto Polit´ecnico de Set´ubal, Portugal

IEETA/Department of Engineering, Portugal

Keywords:

On-the-Person ECG, Low-cost, Open Source, Dynamic Applications, QTc, Heart Rate Variability.

Abstract:

Electrocardiographic (ECG ) data analysis can reveal crucial information about the cardiovascular physiologi-

cal phenomenon, which is modulated by the Autonomic Nervous System. Hereupon, beyond cardiovascular

diagnosis, ECG markers can also reﬂect workload levels, or even physical and mental performance, through

Heart Rate Variabilit y (HRV) analysis. Building upon previous work found within the stat e-of-t he-art, this pilot

research explores the potential of using a low-cost device f or cardiopathy pre-screening, through ECG signal

analysis. With the aim of performing the rhythmical analysis, we performed empirical tests from a population

of 21 control subjects in a resting position, and an additional 2 subjects, one of them in dynamic condition, i n

the scope of an exploratory research, using ECG wave segments analysis and HRV features extraction for nu-

merical analysis. Results have demonstrated that the signal quality allows reliable ECG acquisition for f urther

rhythmical and HRV analysis, in stationary and dynamic monitoring, for the bipolar leads applied. There was

also evidence to suggest a beneﬁt from including ECG morphological analysis wit h this hardware and software

setup for prevention and diagnosis of cardiovascular disorders, although requiring further investigation.

1 INTRODUCTION

Bio-signal analysis with open source and low-cost de-

vices has been increasingly popular in the past deca-

des, as its applications are being recognize d and ex-

tensively explored by the research and industrial en-

gineering ﬁelds. The convergence of synergies bet-

ween such diverse communities has allowed multiple

enabling technolo gies to rea ch great advances in rese-

arch and pro duct development, due to the opportunity

for experimentation given by the low-cost, conﬁgura-

bility and accuracy of the current Do-it-Yourself de-

vices. Impor tantly, the development of proof of con-

cept methodo logies or prototypes for bio-signal appli-

cations can represent a great cost and time reduction

when compared to medical devices. They ca n even

be further enhanced when allied to other are as with

the same philosophy (i.e., low-cost enablers of new

knowledge and experiences), such as 3D printing.

In the landscape of low-cost devices for biome-

dical applications, BITalino (http://bitalino.com/en/)

has been described as a viable choice (Guerreiro e t al.,

2014). Beyond the hardware features, BITalino also

presents a comp rehensive range of software resour-

ces, within which particu la r attention has been given

to the ECG (Silva et al., 2014, 2011; Nˇemcov´a et al.,

2016).

Previous research led us to furth er evaluate this

device, aiming at reinforcing the pieces of evidence to

sustain the reliability f or cardiopathy p re-screening.

As such, for the purpose of this study, the numerical

data from the low-cost device has been empirically

corroborated with the data from a gold standard de-

vice, in or der to evaluate its performance. We aim to

Lourenço, H., Sanﬁns, V., Ala, S., Barros, F., Silva, H. and Reis, M.

Empirical Evaluation of the Potential of Low-cost and Open Source “On-the-Person” ECG for Cardiopathy Pre-screening.

DOI: 10.5220/0006962701150122

In Proceedings of the 5th International Conference on Physiological Computing Systems (PhyCS 2018), pages 115-122

ISBN: 978-989-758-329-2

115

ﬁnd evidences that support th e BITalino’s reliability

for r hythmical and HRV parameters analysis in static

and dynam ic real-world applications. Furthermore,

in the scope of our preliminar y study, the data acqui-

red also sugge st promising results for cardiopathy de-

tection through morpholo gical waveform a nalysis.

For th e se purposes, a control group of 21 subjects

have been monitored with BITalino, using a bip olar

differential lead placement, at rest in supine position,

to guarantee the same conditions that have bee n previ-

ously monitored by a medical team with a gold stan-

dard device. It was also included in this study the

monitoring from two other subjec ts: in one o f the sub-

jects, namely, Subject C2, ECG waveform abnorma-

lities were detected, as well as in one of the subjects

from the control group, designated as Subject C1 a nd

referred in Section 4.1; and the other subjec t, hereaf-

ter referred to as Subject C3, has performed both ECG

monitoring at rest and during trail run, to perform a

preliminar y assessment of the behavior of BITalino in

a dynamic and real-world condition.

The re sults from the rest monitorings point to an

accurate data acquisition. Moreover, the low-cost

device also shows up -and-coming resu lts for further

rhythm ic al and H RV parameters analysis in dynami-

cal on-the-person ECG acquisition , as shown by the

results obtained in case study 3.

2 MOTIVATION

This research builds upo n a study carried out by

Silva et al. (2015), where the authors described a

taxonomy for the practicality of ECG devices, and

performed a numerical comparison of an “off-the-

person” sensor placement with a gold standard ECG

device used in clinical practice. Here, we will add

to this evidence by using BITalin o in a n “on-the-

person” approach compa ring its outpu t with a gold

standard medical device, and also by making a pre-

liminary assessment o f its performance in dynamic

applications (as described in Section 3.1 ). In the

range of non-invasive methods, “on-the-person” ap-

plications show consistent and continuous data acqui-

sition once the system is attached to the body sur-

face, allowing many different application methods.

Portable devices, such as wearables (e.g., Zio TX,

http:// www.irhythmtech.co m/products-services/zio-

xt; ActiHeart, (https://www.camnte c h.com/p roducts/

actiheart/ actiheart-overview) or even used within the

landscape of con ductive textiles (Tong et al., 2018)

can have a great impact for certain age groups that

require a particular approach to improve their adhe-

rence, such as childre n (Z hu et al., 2015) and people

with disabilities.

Regarding ECG and HRV, several studies have

been published using open source and low-cost tools,

including BITalino-based research, revealing that the

high costs of medical and state-of-the -art d evices can

be avoided for several applications (i. e. for proof-of-

concept studies and prototyping development), due to

the accuracy of such devices.

In Alves et al. (2014), BITalino’s ECG perfo r-

mance was tested against a gold standard device —

BIOPAC (https://www. biopac.com/) — aiming to in-

troduce an electrode design for paper-based inkjet

printed electrodes. With a sampling rate of 1000Hz

and 10-bit resolution set up for both devices, the expe-

rimental results showed that the devices had compara-

ble performance in Signal-to- N oise Ratio (SNR) and

Root Mean Square Error (RMSE). Also, the heartbeat

waveform morphology measured with BITalino and

BIOPAC were very close to each oth er.

Silva et al. (2015) also presented a correlation of

ECG data, acquired from 38 voluntee rs at rest, bet-

ween a medical device (Philips PageWriter Trim III

series) and the ﬁrst version of BITalino, aiming to

validate the signal a cquisition accuracy for “off-the-

person” applicatio ns. The medical device used a se-

tup that included the classical 12-lead ECG place-

ment system, whilst BITalino used a single lead, in

a setup with two dry electrodes plac ed at the index

ﬁngers. T he comparative tests showed that the “off-

the-person” ECG data had a prec ision for R-peak de-

tection above 98%, when compared to the correspon-

ding lead in the gold standard device. Additionally,

the segmentation performance and morphological wa-

veform analysis sh owed a strong correlation between

the real-world empirical data assessed for both devi-

ces, reinforcing the potential of low-cost devices.

Concernin g HRV analysis, low-cost and open

source tools have allowed a cost effective and multi-

faceted br oad level of da ta exploitation, which is usu-

ally expensive, limited a nd too generic (Mu˜noz et al.,

2017). In addition, signal post processing and HRV

analysis to extra c t time and frequ ency parameters

through numerical methods, allow the understanding

and use of these data out of lab rooms, beca use their

representation and physiological phenomena auto de-

tection is supported by th is approach (Tarvainen et al.,

2014).

PhyCS 2018 - 5th International Conference on Physiological Computing Systems

116

3 MATERIAL AND METHODS

3.1 Exploratory Study

In order to understand wh ic h leads would best suit the

purposes of our re search (i.e., future implemen tation

in sports, stress tests, or even daily life application s),

factors such as EMG noise (Levick, 2013), lead vec-

tor acco rding to the heart’s electrical condu ctive sy-

stem (Malmivuo et a l., 1995; Dubin, 2000) and lead

sensitivity for ventricular events detection (Fletcher

et al., 2013) were considered. Thus, b ipolar Modiﬁed

Chest Leads MCL

, MCL

, CM

; Modiﬁed L e ads I

and II; and Conventional Lead ( CL) (Francis, 2016;

Dubin, 200 0) were tested. (1) CM

and (2) CL leads

were selected to be used in this stud y due to: lower

EMG artifact susceptibility during limbs m ovement,

(1) R-peak detection, explicit ventricular phe nomena;

(2) lead vector with approximate alignment to the he-

art’s electrica l vector for an overview perspective.

3.2 Volunteers

The study comprised a total of 21 athletes from one

profession al male football team, who trained twice

daily. In this grou p the average age was 21.95±3.32

years old; the average height was 181.3±5.68 cm an d

the average weight was 72.3±5.81 Kg. The athele-

tes declared that they were not under pharmacological

substance that could affect the cardiac phenomenon.

Additionally, a 34 y ear old female with a known

family history of cardiovascular disease, referred to

as subject C2, and a healthy 26 year old male, desig-

nated as subject C3 participated in the study. Results

are reported in case studies 2 and 3, respectively. Sub-

ject C2 is 158 cm height and 47 Kg and subject C3 is

174 cm height an d 70 Kg.

3.3 Experimental Protocol

As part of the Experimental Protocol, all the volun-

teers were individually informed about the procedures

and aim of this study. I n order to avoid any kind of ex-

ternal bio-electrical and ele c tromagnetic interference,

impedance issues related to the skin of the volunteers

and to prop e rly pr epare the volunteers, a ll the proce-

dures and ethical principles stated by Kligﬁeld et al.

(2007); Crawford et al. ( 1999) and the “Helsinki De-

claration of Ethics” were fo llowed. Next, the elec-

trodes were applied in a bipolar conﬁg uration using

leads CM

and CL.

ECG recordin g was performe d at rest in the supine

position and took place before the morning training

and after lunch , befo re the aftern oon train ing so th at

the ECG acquisition could be per formed in basal con-

ditions. Each volunteer was submitted to one ECG

recording, with a minim um duration of 2 mins, accor-

ding to the stated procedures in ESC/AHA ( 1996) for

short-monitoring HRV analysis.

3.4 Acquisition Setup

3.4.1 Hardware

We used a BITalino (r)evolution Plugged Kit, with

two ECG sensors, and 3D printed cases w ere produ-

ced to store the whole kit. The hardware set up inclu-

ded a BITalino (r)evolution main-board, power supp-

lied by a 75 0mAh capacity and 3.7V output LiPo bat-

tery, and communication over Bluetooth to our base

station — a laptop with Windows operatin g system.

Pre-gelled Ag/AgCl elec trodes were used (see Fi-

gure 1). Tab le 1 pre sents the BITalino (r)evolution

ECG sensors speciﬁcations.

Figure 1: (left) BI Talino (r)evolution Plugged Kit (main-

board and ECG sensor connected in t he 3D printed cases);

(right) Example of the bipolar leads electrode placement in

a volunteer.

Table 1: BITalino (r)evolution ECG and BTL-08 MT PLUS

speciﬁcations.

Feature BITalino BTL-08 MT PLUS

Sampling Rate 1000Hz 2000Hz

ADC resolution 10 bit 13 bit

Gain 1100 n.a.

Range ±1.5mV (VCC = 3.3V) AC:±15.9 mV; DC:±400 mV

Bandwidth 0.5 — 40Hz 0.05 — 170Hz

Input Voltage Range ±1.65 V ±5 V

Input Impedance 7.5GΩ > 20MΩ

CMRR 86dB > 98dB

Besides our monitoring, the volunteers were sub-

mitted to ECG monitoring during the football le a gue

pre-season. This was performed using a BTL-08 MT

PLUS for the standard 12-lead ECG acquisition, in

rest.

Empirical Evaluation of the Potential of Low-cost and Open Source “On-the-Person” ECG for Cardiopathy Pre-screening

117

3.4.2 Software

BITalino’s data acquisition software was OpenSig-

nals. The recorded data was per formed at a 1 000Hz

sampling rate. The BTL-08 MT PLUS was set up for

2000Hz and had digital ﬁlters incorporated in the har-

dware — adaptable mains ﬁlter [50-60 Hz]; muscle

tremor ﬁlters for 35 Hz and 25 Hz; baselin e ﬁlters:

0.05 Hz (3.2 s), 0.11 Hz (1.5 s), 0.25 Hz (0.6 s),

0.50 Hz (0.3 s), 1. 50 Hz (0.1 s) and splin e s.

The feature extraction and automatic ECG analy-

sis for the medical device was accomplished through

the BTL CARDIOPOINT ECG C600 software, for

25 mm/s and 50 mm/s r e cording speeds and 10 mm/s

amplitude.

3.5 Data Post-processing

Although the BTL-08 MT PLUS system alre-

ady provides detailed features in the genera te d

reports, BITalino mostly performs raw data acqui-

sition, reason for which data post-processing was

needed. For raw data conversion to the correct

physical units (milliVolt), the transfer fu nction

suggested in BITalino’s manuals was imp le mented

(http://bitalino.com/datash eets/REVOLUTION ECG

Sensor Datasheet.pdf) . Further feature extraction

was performed using the BioSPPy toolbox, a set

of open source and Python -based routines for ECG

signal ﬁltering, R-pea k detection, HR plot, waveform

template (http://biosppy.readthedocs.io/en/stable/).

The BioSPPy toolbox applies a ba nd-pass ﬁlter (3-45

Hz) and also implements Christov’s algorithm for

QRS detection (Christov, 2004). The toolbox was

adapted to obtain the standard ECG trace grid for

25mm/s recording speed and 10mm/mV amplitude,

which improves rhythmical and morphological

analysis by observation, as shown in Figure 2. For

each subject, we have extrac te d ECG traces for 5 s

and 10 s, as well as for co mplete monitoring, for raw

and ﬁltered data, and also the segmented heartbeat

waveforms (Figures 4 & 8) an d heart rate plots

(Figure 7).

HRV feature extraction was acco mplished throu gh

OpenSignals’s add-on, from the raw data.

4 RESULTS

All the components of the P-QRS-T wave, segments

and intervals were detectable and the R-peaks were

explicit in all recorded ECG, as the example shown in

Figure 4.

Figure 2: Portion of the E CG trace with P-QRS-T waveform

identiﬁed in Subject C1 using lead CM

(Section 4.1, be-

low). The inverted T wave and ECG tr ace grid develo-

ped are also represented. Graphical representation extracted

from BioSPP y.

Figure 3: Portion of ECG trace for lead V

and V

re-

corded for Subject C1 adapted from BTL CARDIOPOINT

ECG C600 reports, with a recording speed of 25 mm/s and

10 mm/mV amplitude. This portion of the ECG tr ace evi-

dences the i nverted T wave detected by the gold standard

device.

Figure 4: Example of a set of segmented heartbeat wa-

veforms extracted from the complete ECG monitoring f or

Subject S20 (171.2 s), using lead CL. This graphical repre-

sentation, extracted from the BioSPPy toolbox, represents

the overlap of all (168) ﬁltered P-QRS- T segments template

where the absence of artifacts can be observed.

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118

The ECG acquisition for the ath le tes was perfor-

med in d ifferent days, due to the time required by the

hardware setup procedures that would affect athele-

tes training plan, as well as the medical team availa-

bility for extra days required. Althoug h, the mon i-

toring with the low-cost device was accomplished in

following days, so the physiological condition of the

volunteers could be, theoretically, similar. For these

reasons, the data collected could not be synchronized

between both devices. Also, the medical team only

provide us the ECG reports, instead of the digital data,

which ha s restricted the d ata analysis compon e nt of

our work.

Nonetheless, further analysis of ECG traces f rom

both d evices, namely, the Corrected QT Interval

(QTc) for the heart rate was performed for all athle-

tes (Table 2). The lead selectio n for scalar absolut

QT interval measurement was accomplished accor-

ding to the clinical procedures for th e classic 12- le a d

ECG, where the selected lead re presents the wider

QT interval within the 12 leads. We have followed

the same pro cedure for the modiﬁed leads, acquired

with BITalino. Afterwards, Bazett’s correction for-

mula (QTc = QT /

√

RR [sec]) (Postema and Wilde,

2014) was implemented to return the QTc values.

Table 2: This table presents QTc values and respective ab-

solute QT and RR intervals, through ECG trace scalar mea-

surement. As well as the average RR intervals for t he com-

plete monitoring of both devices, which was obtained f rom

OpenSiganls and BTL CARDIOPOINT ECG C600 auto-

mated extraction.

BITalino (r)evolution BTL-08 MT PLUS

Subject QT

(s)

QTc

(ms)

AVG

(ms)

(s)

QTc

(ms)

AVG

(ms)

S1 0.44 1.04 431 1052 0.4 1.4 338 1333

S2 0.44 1 440 947 0.36 0.86 388 857

S3 0.42 0.92 438 887 0.38 0.86 410 845

S4 0.44 1.04 431 973 0.44 1.12 416 1053

S5 0.48 1.46 397 1433 0.44 1.4 372 1395

S6 0.46 1.16 427 1089 0.44 1.26 392 1224

S7 0.44 1.06 427 1011 0.4 0.88 426 857

S8 0.46 1.04 451 1007 0.4 0.9 422 938

S9 0.4 0.96 408 880 0.36 0.92 375 896

S10 0.44 1.16 409 1086 0.36 0.7 430 706

S11 0.44 0.98 444 940 0.44 0.94 454 1000

S12 0.48 1.2 438 1364 0.44 1.08 423 1091

S13 0.44 1.14 412 1163 0.42 1.4 355 1395

S14 0.42 0.98 424 967 0.4 1.04 392 1000

S15 0.42 1.18 387 1008 0.38 1.32 331 1224

S16 0.44 1.56 352 1434 0.42 1.52 341 1395

S17 0.5 1.22 453 1209 0.46 1.34 397 1277

S18 0.44 1.16 409 1162 0.42 1.52 341 1463

S19 0.4 1.2 365 1029 0.38 0.96 388 938

S20 0.48 1 480 1191 0.44 1 440 1034

S21 0.52 1.44 433 1313 0.44 1.3 386 1224

AVG 0.44 1.14 427 1052 0.42 1.08 392 1053

SD 0.0306 0.172 29.55 169.5 0.0314 0.248 35 .77 221.8

The following subsections describe the case stu-

dies b ased on ECG monitoring using BITalino, gui-

ded throug h the sam e protoco l and tools as the main

group of 21 volunteers, including possible cardio-

pathy events for the c ontrolled group. Even though

the aim of this study was to assess BITalino ’s reliabi-

lity for rhythmic analysis, curious evidences in wave

morphology were detected and are discussed in furt-

her detail below.

4.1 Case 1 — Inverted Polarity in T

Wave

In the ECG acquisition in one of th e athletes, Subject

C1, an abnormal waveform was detected (Figure 2).

This fact was em pirically corr oborated using the data

from the medical device, which showed that th e in-

verted pola rity of the T wave was also de tected by

BTL-08 MT PL US in precordial leads V

and V

(Fi-

gure 3), but also in lead V

. This evidence may sug-

gest a variety of c a rdiac disorde rs, although further

clinical an alysis ( i.e., echography) will have to be per-

formed, to accomplish a proper diagnostic and to ex-

clude ECG pattern altera tions related to different phy-

sical activities or certain age, gender or race groups

(Drezner et al., 2013; Macfarlane et al., 2014).

4.2 Case 2 — Extrasystole Detection

During the exploratory phase of this study, Lead I —

an Eithoven-like setup with two electrodes placed on

the wrists, for ECG monitoring in an upright seated

position — has been tested in Subject C2.

Figure 5: Portion of ECG strip showing extrasystole de-

tected by BITalino, on Lead I, for Subject C2.

Figure 6: Extrasystole detected during the Holt er exam, re-

corded at 25 mm/s of speed and 10 mm/mV in amplitude.

During the ECG monitoring, acquired with BITa-

lino, abnormal events were de te cted and later conﬁr-

med by a trained physician, who advised the subject

Empirical Evaluation of the Potential of Low-cost and Open Source “On-the-Person” ECG for Cardiopathy Pre-screening

119

to be further examined at the local cardiology ser-

vice. As a result, Subject C2 has performed a Holter

exam — using a NovaCor device and HolterSoft Ul-

tima V2.4.4 software — in which the medical team

diagnosed extrasystoles. Afterwards, we investigated

the results obtained in both tests, which can be obser-

ved in Figures 5 & 6.

4.3 Case 3 — HRV Analysis

Subject C3 perf ormed ECG acquisition at rest and

during trail running, in the scope of the exploratory

phase of this study. T he ha rdware set up was attached

to the subject’s clothes using a 3D printed clip, and

the cables and electrodes were ﬁxed with an elastic

net tube bandage. An Android smartphone was used

as the base station. Figure 7 represents the HR graph,

during a portion of the trail run, which coincide d with

the end of the run. In Figu re 8, the segmented in-

dividual heartbeat waveforms are represented for the

overall run.

Figure 7: Portion of approximately 18 mins (1108.4 s) for

HR analysis. Graph extracted through the BioSPPy toolbox

for Subject C3, during a trail running.

Figure 8: Segmented heartbeat waveforms for Lead

during the tr ail running, for approximately 18 min

(1108.4 s). Signal ﬁltered and graph generated using Bi-

oSPPy.

5 DISCUSSION

The exploratory study with Subject C3 has revealed

that even under upper limbs and trunk muscles con-

traction, as well as in the presence of perspiration

and all the evoked potentials spre a d by the muscular

groups surrounding th e electrodes, the R-peaks can be

Table 3: HRV time parameters extracted from CM

lead for

Subject C3.

Time parameter Rest Run

Min. NN (ms) 775 344

Max. NN (ms) 937 945

Avg. NN (ms) 858 492

SD NN (ms) 30 74

rMSSD (ms) 28 21

NN20 99 389

pNN20 (%) 58 17

NN50 11 61

pNN50 (%) 6 2

Avg. IHR (BPM) 69 121

SD IHR (BPM) 2 19

Table 4: HRV non-linear parameters extracted from CM

lead for Subject C3.

Non-linear parameter Rest Run

SD1 (ms) 20 15

SD2 (ms) 39 104

SD1/SD2 0.51 0.14

Table 5: HRV frequency parameters extracted from CM

Lead for Subject C3.

Parameter

Rest Run

VLF LF HF VLF LF HF

Frequency

(Hz)

0–0.04 0.04–0.15 0.15–0.4 0–0.04 0.04–0.15 0.15–0.4

Peak

(Hz)

0.007 0.062 0.338 0.012 0.06 0.152

Power

(ms

)

122 307 300 1890 472 227

Power

(%)

17 42 41 73 18 9

Power

(n.u.)

– 51 49 – 68 32

Figure 9: Power spectral density (PSD) (left) and Poincar´e

(right) plots for S ubject C3 during rest.

Figure 10: Power spectral density (PSD) (left) and Poincar´e

(right) plots for S ubject C3 during trai l run.

effectively detected by Lead CM

for long dynamic

monitoring, as shown in Figures 7 & 8.

Regarding the QTc measuremen t, procedures

found in the state-of-ar t and clinical practice stipu-

late tha t this measurements sho uld be performe d by

using the classic 12-lead ECG analysis. However, the

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120

results from the modiﬁed leads show approx imated

values. Further investigation is needed to evaluate if

a range of appropriate cut-off values allows ac c urate

diagnosis from QTc values obtained from modiﬁed

leads.

Even though there is a historical non-consensual

objectivity for HRV p arameters analysis (Trimmel

et al., 2015), there are emerging pieces of evidence

that support its use for prevention and/or monitoring

of mental, phy sical and physio logical health c onditi-

ons (Hughes e t al., 2010; Taelman et al., 200 9), as

well as in sports for physical and mental performance

improvement (Pec¸anh a et al., 2013; Dong, 2016).

HRV parameters for th e control group (Table 6),

the overall average values for rest acquisition sugge -

sted a good level of parasympathetic predominance.

However, there were some cases th at would have gre -

atly beneﬁted from continuous monitoring to improve

the sympathovagal balance , and further physical per-

formance improvemen t. It is important to note that

the measures were acquired in the beginnin g of the

football season, which meant that some of the athle-

tes were just restarting their professional training an d

the physical performance was n ot at its peak. HRV

analysis must be regular, so the data can support con-

sistent results and avoid events that can change the

physiological phenomena.

Table 6: The overall average values of HRV parameters for

the control group, extracted from CL (the value of 8.1 for

the LF/HF ratio is an isolated value).

(ms)

AVG

IRH

(BPM)

IHR

(BPM)

LH / HF

ratio

SD1/SD2

Min. 30 41 1 0.2 0.19

Max. 162 68 7 8.1 1.13

Average 79 55 3.7 1.14 0.64

By analyzing HRV parameters in rest and during

trail running (Tables 3, 4 & 5) for Subject C3, we

observed the sympa thetic predominance during exe-

rcise. The physical need s of the body during the ae-

robic exercise (i.e., oxygen a bsorption and car bon di-

oxide excretion, energy consumption, etc.) were re-

ﬂected in LF predominance, besides HR increm e nt,

which increased the LF/HF ratio — PSD graphs in

Figures 9 & 10 present the LF predomin a nce during

exercise.

Also, the non-linear parameters showed the same

sympathovagal balance alteration. During exercise

the rMSSD had also decreased, due to the decrea-

sing parasympathetic activity. As stated by Dong

(2016), we conﬁrmed the regularity of the heart be-

ats and sympa thetic predominance during exercise, as

demonstra te d in the Poincar´e plots in Figures 9 & 10.

6 CONCLUSIONS AND FUTURE

WORK

We have presen ted an evaluation of a low-cost and

DiY device when compar e d to a medical-grade sy-

stem to assess the potential of the f ormer for car di-

opathy pre-scr eening, by performing tests in a total

population of 23 subjects. Results have demonstrated

that the sig nal quality allows reliable ECG acquisition

for further rhythm and HRV analysis, in stationary

and dynamic monitoring, using the bipolar leads sen-

sor conﬁguration. Also, we found evidence to support

the use of ECG morphological analysis in prevention

and diagnosis of cardiac diso rders.

From a rhythmical point of view, the low-cost

device has shown promising results. The case stu-

dies discussed and the results obtained also motivated

our team to investigate BITa lino’s potential consis-

tency for abnormal waveform pattern detection in se-

vere pathologies and d emanding environmental con-

ditions, towards its maximum usability for rhythmical

and morpholog ic al ECG analysis.

In the future, we a im to further test the reliabi-

lity of BITalino to monitor subjects with diagno sed

rhythm ic al cardiac disorders, both during rest and

stress tests. Patients will be monitored with a syn-

chronize d go ld standard device, in order to assess the

accuracy that can be achieved with BITalino. We will

evaluate the rhythmical analysis, including HRV for

continuous monitor ing, and wave morpholo gy analy-

sis in this context. With the synchronized acquisition

setup, we will also be able to determine whether a cut-

off value can be set for QTc measurement accuracy

with modiﬁed leads. I n addition, other bipolar lead s

will be explored to attain different electrical le ad vec-

tors, so the analysis of the heart atr ia can be improved,

as well as new approache s for LF/HF ratio analysis

that can better track mental and emotional states, in a

similar fashion to the method developed by von Ro-

senberg et al. (2017).

REFERENCES

Alves, A. P., Martins, J., Sil va, H. P., Lourenc¸o, A., Fred,

A., and Ferreira, H. (2014). Paper-based inkjet elec-

trodes. In Silva, H. P., Holzinger, A., Fairclough, S.,

and Majoe, D., editors, Physiological Computing Sys-

tems, pages 59–70. Springer Berlin Heidelberg.

Christov, I. I. (2004). Real time electrocardiogram QRS

detection using combined adaptive threshold. Biome-

dical engineering online, 3(1):28.

Crawford, M. H., Bernstein, S. J., Deedwania, P. C., Di-

Marco, J. P., Ferrick, K. J., Garson, A., Green, L. A.,

Greene, H. L., Silka, M. J., Stone, P. H., Tracy, C . M.,

Empirical Evaluation of the Potential of Low-cost and Open Source “On-the-Person” ECG for Cardiopathy Pre-screening

121

Gibbons, R. J., Alpert, J. S., Eagle, K. A., Gardner,

T. J., Garson, A., Gregoratos, G., Russell, R. O., Ryan,

T. J., and Smith, S. C. (1999). ACC/AHA guidelines

for ambulatory electrocardiography: Executive sum-

mary and recommendations. Circulation, 100(8):886–

893.

Dong, J.-G. (2016). The role of heart rate variability in

sports physiology. Experimental and Therapeutic Me-

dicine, 11(5):1531–1536.

Drezner, J. A., Ackerman, M. J., Anderson, J., Ashley, E.,

Asplund, C. A., Baggish, A. L., B¨orjesson, M., Can-

non, B. C., Corrado, D. , DiFiori, J. P., Fischbach, P.,

Froelicher, V., Harmon, K. G., Heidbuchel, H., Marek,

J., Owens, D. S., Paul, S., Pelliccia, A., Prutkin, J. M.,

Salerno, J. C., Schmied, C. M., Sharma, S., Stein, R.,

Vetter, V. L., and Wilson, M. G. (2013). Electrocardio-

graphic interpretation in athletes: the ‘seattle criteria’.

British Journal of Sports Medicine, 47(3):122–124.

Dubin, D. (2000). Rapid interpretation of EKG’s: an i nte-

ractive course. Cover Pub. Co, USA, 6th edition.

ESC/AHA (1996). Heart rate variability. Circulation,

93(5):1043–1065.

Fletcher, G. F., Ades, P. A., Kligﬁeld, P., Arena, R., Balady,

G. J., Bittner, V. A ., Coke, L. A., Fleg, J. L., Forman,

D. E., Gerber, T. C., Gulati, M., Madan, K., Rhodes,

J., Thompson, P. D., and Williams, M. A. (2013). Exe-

rcise standards for testing and training. Circulation,

128(8):873–934.

Francis, J. (2016). ECG monitoring leads and special le-

ads. Indian Pacing and Electrophysiology Journal,

16(3):92 – 95.

Guerreiro, J., Lourenc¸o, A., Silva, H., and Fred, A. (2014).

Performance comparison of low-cost hardware plat-

forms targeting physiological computing applications.

Procedia Technology, 17:399 – 406. Conf. on Electro-

nics, Telecom. & Computers (CETC 2013).

Hughes, J. W., Casey, E., Doe, V. H., and Glickman, E. L.

(2010). Depression and heart rate variability in cardiac

rehabilitation patients: Exploring the r oles of physi-

cal activity and ﬁtness. Perceptual and Motor Skills,

111(2):608–624.

Kligﬁeld, P., Gettes, L. S., Bailey, J. J. , Childers, R., Deal,

B. J., Hancock, E. W., van Herpen, G., Kors, J. A.,

Macfarlane, P., Mirvis, D. M., Pahlm, O., Rautaharju,

P., and Wagner, G. S. (2007). Recommendations for

the standardization and interpretation of the electro-

cardiogram. Circulation, 115(10):1306–1324.

Levick, J. (2013). An Introduction to Cardiovascular Phy-

siology. Elsevier Science.

Macfarlane, P. W., Katibi, I. A., Hamde, S. T., Singh, D.,

Clark, E. B., Devine, B., Francq, B. G., Lloyd, S. A.,

and Kumar, V. (2014). Racial differences in the ECG–

selected aspects. Journal of electrocardiology, 47

6:809–814.

Malmivuo, P., Malmivuo, J., and Pl onsey, R. (1995). Bioe-

lectromagnetism: Principles and Applications of Bi-

oelectric and Biomagnetic Fields. Oxford University

Press.

Mu˜noz, J., Gouveia, E., Cameir˜ao, M., and Berm´udez i Ba-

dia, S. (2017). Physiolab - a multivariate physiological

computing toolbox for ECG, EMG and EDA signals:

a case of st udy of cardiorespiratory ﬁtness assessment

in the elderly population. Multimedia Tools and Ap-

plications, pages 11521–11546.

Nˇemcov´a, A. , Marˇs´anov´a, L., Smisek, R., Vitek, M., and

Kol´aˇrov´a, J. (2016). Recommendations for ECG

acquisition using BITalino. In EEICT C onf., pages

543–547.

Pec¸anha, T., de Paula-Ribeiro, M., Nasario-Junior, O., and

de Lima, J. R. P. (2013). Post-exercise heart rate vari-

ability recovery: a time-frequency analysis. Acta Car-

diologica, 68(6):607–613.

Postema, P. G. and Wilde, A. A. (2014). The measure-

ment of the QT interval. Current Cardiology Reviews,

10(3):287–294.

Silva, H., Lourenc¸o, A., Fred, A. L. N ., and Martins,

R. C. M. (2014). BIT: Bi osignal igniter toolkit.

Computer Methods and Programs in Biomedicine,

115(1):20–32.

Silva, H., Lourenc¸o, A., and Paz, N. (2011). Real-time bio-

signal acquisition and telemedicine platform for AAL

based on Android OS. In Int. Living Usability Lab

Workshop on AAL Latest Solutions, Trends and Appli-

cations.

Silva, H. P., Carreiras, C., Lourenc¸o, A., Fr ed, A., Neves,

R. C., and Ferreira, R. (2015). Off-the-person elec-

trocardiography: performance assessment and clinical

correlation. Health and Technology, 4(4):309–318.

Taelman, J., Vandeput, S., Spaepen, A., and Van Huffel, S.

(2009). Inﬂuence of mental stress on heart rate and

heart rate variability. In Vander Sloten, J., Verdonck,

P., Nyssen, M., and Haueisen, J., editors, 4th Euro-

pean Conf. Int. Federation for Medical and Biological

Eng., pages 1366–1369.

Tarvainen, M. P., Niskanen, J.-P., Lipponen, J. A., Ranta-

aho, P. O., and K arjalainen, P. A. (2014). Kubios HRV

– heart rate variability analysis software. Computer

Methods and Programs in Biomedicine, 113(1):210 –

220.

Tong, W., Kan, C., and Yang, H. (2018). Sensitivity analysis

of wearable textiles for E CG sensing. In Proc. Int.

Conf. I EEE EMBS on Biomedical Health Informatics

(BHI), pages 157–160.

Trimmel, K., Sacha, J., and Huikuri, H. (2015). Heart Rate

Variability: Clinical Applications and Interaction bet-

ween HRV and Heart Rate. Frontiers Research Topics.

Frontiers Media SA.

von R osenberg, W., Chanwimalueang, T., Adjei, T., Jaffer,

U., Goverdovsky, V., and Mandic, D. P. (2017). Re-

solving ambiguities in the LF/HF ratio: LF-H F scat-

ter plots for the categorization of mental and physical

stress from HRV. Frontiers in Physiology, 8:360.

Zhu, Z., Liu, T., Li, G., Li, T., and Inoue, Y. (2015). Weara-

ble sensor systems for infants. Sensors, 15(2):3721–

3749.

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