Heart Rate Variability and Electrodermal Activity in Mental Stress

Aloud: Predicting the Outcome

Rodrigo Lima

1,2

, Daniel Os

orio

1,3

and Hugo Gamboa

1,2,3

Plux-Wireless Biosignals S.A, Avenida 5 de Outubro 70, 1050-59, Lisboa, Portugal

Department of Physics, Faculdade de Ci

encias e Tecnologia da Universidade Nova de Lisboa,

Monte de Caparica, 2892-516, Caparica, Portugal

Laborat

orio de Instrumentac¸

ao, Engenharia Biom

edica e F

ısica da Radiac¸

ao (LIBPhys-UNL),

Faculdade de Ci

encias e Tecnologia da Universidade Nova de Lisboa, Monte de Caparica, 2892-516, Caparica, Portugal

Keywords:

Heart Rate Variability, Electrodermal Activity, Photoplethysmography, Autonomous Nervous System,

Wearable Device, Biosignals, Machine-Learning, Classiﬁcation.

Abstract:

The assessment of changes in the autonomous nervous system (ANS), have important prognostic and diag-

nostic value, and can be used to assess stress levels. There are many approaches to directly measure the

sympathetic and parasympathetic nervous system, although, most of them are invasive and unable to provide

continuous monitoring. Heart rate variability (HRV) and Electrodermal activity (EDA) are noninvasive meth-

ods to assess the autonomous nervous system, by computing the spectral analysis of both HRV and EDA

biosignals. In order to provide continuous monitoring, a wearable device is used, obtaining HRV features with

photoplethysmography signals from the wrist and EDA from the ﬁngers. The extraction of the HRV and EDA

features, were obtained by submitting the subjects to a mental arithmetic stress test. The distinct response

to stress was then classiﬁed using machine-learning techniques. The constructed models have the ability to

predict how the subjects will respond, with an accuracy of approximately 80% in terms of HRV features in

baseline and an accuracy of approximately 77% in terms of HRV and EDA simultaneous baseline features,

when submitted to a situation of stress.

1 INTRODUCTION

The assessment of the changes in the autonomous ner-

vous system (ANS) activity related with certain dis-

eases and pathologies, such as myocardial infarction,

cardiac transplantation, myocardial dysfunction, di-

abetic neuropathy and depression, has been demon-

strated to have important prognostic and diagnostic

value (Posada-Quintero et al., 2016a).

In recent times, cardiovascular research has

played an important role in studying the activity of

the ANS, so delineating the role of autonomous car-

diac reactivity is important to prevent these serious

health diseases (Posada-Quintero et al., 2016a).

The ANS is regulated by the central autonomous

network in the brain, comprised of multiple neu-

roanatomical structures. These brain related struc-

tures inﬂuence heart activity, responding and adapt-

ing to environmental challenges, through the adjust-

ment of physiological arousal by transmitting output

to the sinoatrial node of the heart (Hamilton and Al-

loy, 2016).

The autonomous signals are transmitted to

the body through two branches of the ANS:

the sympathetic nervous system (SNS) and the

parasympathetic nervous system (PNS). The sympa-

thetic and parasympathetic nerve ﬁbers secrete two

synaptic transmitter substances: acetylcholine and

epinephrine. The terminal nerve endings of the PNS

secrete acetylcholine, also called cholinergic ﬁbers,

thus its inﬂuence on heart rate is mediated via re-

lease of acetylcholine by the vagus nerve, decreasing

the strength of contraction and consequent heart rate

(Hamilton and Alloy, 2016). The terminal endings

of the SNS secrete epinephrine, also called adren-

ergic ﬁbers, a term derived from adrenalin, thus its

inﬂuence on the heart is mediated via release of

epinephrine, increasing the force of contraction and

consequent heart rate (Guyton and Hall, 2011). In a

situation of stress, usually, vagal activity withdrawals,

decreasing the control and inﬂuence on the heart by

the vagus nerve, facilitating the activation of the SNS,

with excitatory inﬂuences to the heart.

There are many approaches to directly measure

the PNS and SNS activity, although, most of them are

invasive and unable to provide continuous monitor-

Lima, R., Osório, D. and Gamboa, H.

Heart Rate Variability and Electrodermal Activity in Mental Stress Aloud: Predicting the Outcome.

DOI: 10.5220/0007355200420051

In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 42-51

ISBN: 978-989-758-353-7

ing, leading to an inaccurate assessment of the ANS

dynamics (Posada-quintero and Hall, 2016). A non-

invasive method to assess the ANS activity, is to com-

pute the power spectral analysis of Heart Rate Vari-

ability (HRV) (Posada-Quintero et al., 2016a; Posada-

Quintero et al., 2016b; Bansal et al., 2009).

HRV is a measure of the time series of beat-to-

beat intervals from an electrocardiogram (ECG) be-

tween consecutive heart beats (Zoltan, 2013). HRV

can also be computed by acquiring photoplethysmog-

raphy (PPG) signals.

PPG is an optical measurement technique, used

to detect blood volume changes in the microvascu-

lar bed of tissue, with widespread clinical application,

such as ambulatory patient monitoring (Allen, 2007;

Bolanos et al., 2006). PPG signals are a source of

HRV information due to the synchronization between

heart beats in the ECG and the systolic peak in the

PPG waveform.

In order to monitor the effects of neural mecha-

nisms to the heart, the spectral analysis of HRV has

been performed to asses the level of unbalance of the

ANS. The PNS is a major contributor to the high fre-

quency (HF) component (0.15-0.4 Hz), while the low

frequency (LF) component (0.04-0.15 Hz) is consid-

ered to be a marker of the sympathetic modulation,

despite being inﬂuenced by both the PNS and the

SNS (Zoltan, 2013; Bussmann, 1998; Miranda Dan-

tas et al., 2012). The ratio LF/HF reﬂects the balance

between the sympathetic and parasympathetic activ-

ity, so it has not been fully accepted as an accurate

measure of the ANS, since the LF component is also

inﬂuenced by the parasympathetic system (Posada-

Quintero et al., 2016a).

Electrodermal Activity (EDA) is an alternative

method to directly assess the SNS (Kleckner et al.,

2017). The human skin is innervated by numer-

ous efferent ﬁbers, including sympathetic ﬁbers, such

as eccrine sweat glands, which produce sweat when

the acetylcholine transmitter passes from sudomotor

ﬁbers to these glands, changing the skin’s electri-

cal characteristics (Boucsein, 2012). Eccrine glands

are mostly involved in emotional responses to exter-

nal stimulus and reﬂect only activity from the SNS,

because there is no innervation of the PNS in these

glands (Posada-quintero et al., 2016).

EDA signals can be divided into two different

components: phasic component (SCR - Skin Conduc-

tance Response) and the tonic component (SCL - Skin

Conductance Level) (Gamboa and Fred, 2008). The

phasic component is the result of the activation of the

SNS, after a stimuli presentation, being usually over-

lapped by the tonic component, which is not directly

related to an external stimuli, because it is a slow

changing signal (Benedek and Kaernbach, 2010).

To establish a connection between both tech-

niques, each subject was submitted to a mental arith-

metic stress test, the Paced Visual Serial Addition Test

(PVSAT). The PVSAT is the visual version of the

PASAT, a test where the participants are presented

with a series of digits that must be summed in a

narrow time interval. The participants must respond

aloud the correct answer, prior to the presentation of

the next digit (Tombaugh, 2006; Royan et al., 2004;

Parsons and Courtney, 2014), triggering a state of

anxiety and stress among the participants, increasing

heart rate and electrodermal activity, making it eas-

ier to detect changes in the SNS and PNS, during the

situation of stress compared to baseline.

This paper is divided in 4 sessions. In the next

session the materials and methods are presented, with

the description of the population, materials and pro-

tocol of the stress test performed. The methods used

to analyze the data are also described in this session,

with details of the algorithms used to compute HRV

and EDA features. Additionally, a statistical analysis

and machine-learning algorithms are also presented.

Then, the results obtained are presented in session 3,

with the classiﬁcation and construction of the models

to predict the outcome. Finally, the results obtained

are discussed in session 4.

2 MATERIALS AND METHODS

2.1 Study Population

Data was acquired from a group of volunteer subjects.

Fifteen participants (9 females and 6 males) of ages

from 21 to 55 years old (31 ± 11), height from 1.57

to 1.85 meters (1.73 ± 0.09) and weight from 52 to

94 kilograms (72 ± 13) signed an informed consent.

Table 1 gives the statistics for the study population.

Table 1: Study population statistics.

Mean SE Min Max

Age (years) 31 11 21 55

Height (m) 1.72 0.09 1.57 1.85

Weight (kg) 72 13 52 94

SE - Standard Error

2.2 Materials

The acquisition of the biosignals was made with a

BITalino wearable wrist device prototype composed

of six different sensors: EDA wrist, PPG, Spare sen-

sor, Total Volatile Organic Compounds (TVOC), Car-

Heart Rate Variability and Electrodermal Activity in Mental Stress Aloud: Predicting the Outcome

bon Dioxide (CO2) and Temperature (TEMP), devel-

oped by Plux Wireless Biosignals (see Table 2). For

this experiment only the PPG (Channel 2) and the

EDA spare sensor (Channel 3) were used. Both the

PPG and EDA signals were acquired, simultaneously,

with a sampling rate of 1000 Hz and 10-bit resolution.

The PPG sensor is a green LED with a photodetector

in reﬂection mode while the EDA sensor uses gelled

electrodes.

Table 2: Wearable Wrist Device Speciﬁcations.

Sensor Channel Resolution (bits) Sampling Rate

EDA wrist 1 10

10 Hz

100 Hz

1000 Hz

PPG 2 10

Spare 3 10

TVOC 4 10

CO2 5 6

TEMP 6 6

2.3 Protocol

The experiment was performed in a quiet room, in or-

der to avoid interference that would distract the par-

ticipants, due to the fact that, in order to perform the

PVSAT several cognitive functions are required, such

as attention and working memory. The duration of the

experiment was 12 minutes (6-min baseline + 6-min

stress). The stress status is deﬁned by the changes

in physiological parameters derived by the complex-

ity and difﬁculty of the PVSAT, in comparison with

the baseline status. The subjects were asked to sit in a

comfortable chair and avoid any movement during the

entire experiment, specially in the left arm. PPG and

EDA signals were recorded simultaneously using the

wearable device described in section 2.2. The EDA

signals were recorded attaching the electrodes to the

anterior middle phalanges of the 2nd ﬁnger (Position

1 in Fig.1(a) ) and 3rd ﬁnger (Position 2 in Fig.1(a) )

of the left hand. The PPG signal was recorded on the

posterior distal left wrist, as shown in Fig.1(b).

After placing the wearable device and the EDA

spare sensor, the PVSAT test was explained to the

subjects. The PVSAT was presented to induce stress

in the last 6-min, in a 12.2” tablet with white single

numbers from 1 to 9, on a black screen. The digits

were presented with a 3s rate for the ﬁrst 2min, de-

creasing half a second every two minutes (2.5s and

2s). The subjects had to respond prior to the presenta-

tion of the next digit, and speak aloud each response.

A warning 30s before the beginning of the PVSAT

was given to all participants (Blue line in Fig.2). In

baseline status, the subjects were asked not to speak.

(a) EDA. (b) PPG.

Figure 1: Recording sites for the biosignals.

Figure 2: Representation of the warning and start of the

PVSAT. EDA signal increases at the warning 30s before the

PVSAT starts (Blue line). The start of the PVSAT is repre-

sented by the Red line (Brennan et al., 2001).

2.4 Data Processing

2.4.1 PPG Peak Detection

HRV features were acquired with PPG signals. In or-

der to detect the systolic peaks in the PPG, the al-

gorithm implemented is based on the work perfomed

by (Kuntamalla et al., 2014). This algorithm applies

a 2nd order lowpass Butterworth ﬁlter at 2 Hz, fol-

lowed by a 2nd order highpass Butterworth ﬁlter at

0.1 Hz. Then it detects the peaks and valleys of the

PPG wave, and computes the difference in amplitude

between the peaks and the valleys. After calculating

this difference, the algorithm will search for the dif-

ferences that are greater than 50% of a 5-point win-

dow moving average, discarding the peaks that do not

satisfy this criteria. This process is then repeated un-

til the number of peaks between two consecutive it-

erations is the same. The systolic peaks detected are

shown in Fig.3.

2.4.2 Heart Rate Computation

Heart rate is obtained by calculating the interval be-

tween two consecutive systolic peaks, detected with

the algorithm in section 2.4.1. In order to remove arti-

facts inﬂuence or errors in the detection of the peaks,

RR intervals lower than 380 ms were removed due

to physiological conditioning, as a normal heart cy-

cle lasts at least 380 ms. The instantaneous heart rate

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

Figure 3: Peaks Detected (Red dots) with the algorithm im-

plemented.

(IHR) in beats per minute (Bpm) is given by equation

IHR (Bpm) =

∆RR

; ∆RR = RR

− RR

i−1

; (1)

2.4.3 RR-interval Series Filtering

RR-interval series recorded from a wearable device

PPG sensor are subject to different kinds of artifacts

(Jang et al., 2014), as the most common are motion ar-

tifact, breathing artifact and ectopic beats, leading to

a wrong detection of the R-peak (Logier et al., 2004).

To correct the miscalculated peak, a 7-point mov-

ing average window was computed. If a RR-interval

differs more than 20% of the moving average, or if

the RR

i+1

is smaller than 75% of the value RR

i−1

those points are considered as a wrong detection (Lo-

gier et al., 2004). Then, a linear interpolation is com-

puted to replace each interval considered as a wrong

detection.

2.4.4 HRV Features

Time-domain features and frequency-domain were

calculated to quantify HRV, in 5-min segments for

baseline and stress. The time between the 5th and

7th minute, was considered to be the transition band,

where heart rate changes signiﬁcantly from baseline

to stress (Fig.4).

Figure 4: HRV data division in 5-min segments. Baseline

status - 0-min to 5-min (Green band), Stress status - 7-min

to 12-min (Yellow band). The Red band corresponds to the

excluded Transition band.

Statistical features related to the variance of

RR-intervals, such as SDNN, RMSSD, NN50

and pNN50, were computed for 5-min recordings

(Vollmer, 2015; Guidelines, 1996).

The 5-min Poincar

e plot represents the diagram in

which each RR interval is plotted against the previous

RR interval. From the Poincar

e plot it is possible to

extract non-linear variables, such as SD1, SD2 and

SD2/SD1 ratio, that reﬂects the balance between the

SNS and PNS (Hsu et al., 2012).

Frequency-domain features were also computed.

The RR-interval series are an irregularly time-

sampled series, though it is necessary to resample the

series, to avoid the appearance of additional harmonic

components in the power spectrum. Resampling was

performed at a frequency of 10 Hz.

The power spectrum for baseline status (at rest)

and stress status (perfoming the PVSAT) (Fig.5), was

computed using a periodogram, applying to each seg-

ment, a Hanning window. Then, the Fast Fourier

Transform (FFT) was calculated for each windowed

segment. Very-low (VLF), low (LF), high (HF) fre-

quency components and total power were obtained by

integrating the power in each frequency band. The

normalized frequency components were calculated by

dividing the LF and HF power, by the total power mi-

nus the power of the VLF band (Guidelines, 1996).

Figure 5: HRV power spectrum. The left spectrum corre-

sponds to a Baseline status and the rigth spectrum corre-

sponds to the Stress status. VLF (0.0033-0.04 Hz) - Red

band, LF (0.04-0.15 Hz) - Green band, HF (0.15-0.4 Hz) -

Yellow band.

2.4.5 EDA Features

In terms of EDA recordings, time-domain features

were computed, by dividing the data into four seg-

ments: two bands of 2-min each in baseline (Baseline

1, Baseline 2) and in stress (Stress 1, Stress 2). The

Red band in Fig.6, 4th to 6th minute, was considered

the transition band, where EDA level changes signif-

icantly, due to the warning of the start of the PVSAT

test, 30s before the start.

Time-domain features were extracted by applying

a 4th order lowpass Butterworth ﬁlter at 1 Hz. Then

Heart Rate Variability and Electrodermal Activity in Mental Stress Aloud: Predicting the Outcome

Figure 6: EDA data division in 2-min segments. Baseline

1 - 0-min to 2-min (Green Band), Baseline 2 - 2-min to 4-

min (Blue band), Stress 1 - 6-min to 8min (Yellow band),

Stress 2 - 8-min to 10-min (Purple band). The Red band

corresponds to the Transition band.

the model proposed by (Gamboa and Fred, 2008),

computed the SCR component. From the SCR wave-

form, time domain features such as SCR amplitude,

Rise time, Recovery Time 50% (Rec.t 50%) and Re-

covery Time 63% (Rec.t 63%), were obtained as

shown in Fig.7. A threshold of 0.005 µS was applied.

The SCL component was obtained by subtracting the

total EDA signal by the SCR component.

Figure 7: SCR features. The green mark corresponds to

1% of the maximum value (t0), the red mark corresponds

to the maximum value of the peak (t2), the purple and the

black mark correspond to the values in which the amplitude

decreases, respectively, 50% and 63%.

Frequency-domain analysis was also performed

(Posada-Quintero et al., 2018). After ﬁltering the

EDA signal, the signals was downsampled. Down-

sampling from 1000 Hz to 1 Hz was performed in

three steps using consecutive factors of 1/10. Then

the signals was highpass ﬁltered with a 8th order But-

terworth ﬁlter at 0.01 Hz, to remove any trend.

The power spectrum was computed using a peri-

odogram, applying to each segment, a Blackman win-

dow. Then, the FFT was calculated for each win-

dowed segment. The frequency band to assess the

activity of the SNS through EDA used by Posada-

Quintero et al., was modiﬁed to the frequency band

of 0.04-0.35 Hz. Finally, the power for Band 1 (0.04-

0.35 Hz) and Band 2 (0.35-0.50 Hz). The normal-

ized frequency components were calculated by divid-

ing Band 1 and Band 2 power, by the total power,

to verify if there was an increase in power on Band

1 during the stress situation, in order to conﬁrm the

stimulation of the SNS (Fig.8).

Figure 8: EDA power spectrum. VLF (0-0.045 Hz) - Red

band, Band 1 (0.045-0.35 Hz) - Green band, Band 2 (0.35-

0.5 Hz) - Yellow band.

2.5 Statistical Analysis

Statistical tests analysis were performed in order to

assess the signiﬁcance of the results obtained, be-

tween the baseline and stress features.

Kruskal-Wallis test is a non-parametric test, so

it means that is does not assume the normality of

data nor the homoscedaticity (standard deviation are

equal). The H-test uses ranked values, so the values

observed are converted to their ranks. The Kruskal-

Wallis null-hypothesis is that the mean ranks of the

different groups are the same (McDonald, 2014).

Probabilities lower than the signiﬁcance level of 5%

(p-value< 0.05) were considered signiﬁcant, con-

cluding that the null hypothesis may not adequately

explain the observation - there is in fact variation be-

tween the ranked means of the groups.

Chi-square test χ

was also applied to test the

goodness of ﬁt in section 3.2. This test is applied to

determine whether a categorical variable from a sin-

gle population is consistent with a hypothesized dis-

tribution. The null hypothesis is that the categorical

data has the given frequencies (Cochran, 2013). In

the context of this paper, the χ

test will be applied

to determine the goodness of the ﬁt of the linear re-

gression line performed, by comparing the values ob-

served calculated using the regression line obtained,

with the expected values. Probabilities higher than the

signiﬁcance level of 5% (p-value< 0.05) lets us con-

clude that the difference between the observed values

and the expected values is minimized, so the linear

regression is a good ﬁt.

2.6 Machine-Learning

Machine-learning algorithms were applied in order to

classify the data.

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

Support Vector Machines (SVM) algorithms for

learning two-class discriminant functions from a set

of training examples were applied, in order to ﬁnd a

suitable boundary (hyperplane), in data space to sep-

arate two classes. The basis of this boundary is the

concept of margin, which is the minimal distance be-

tween the hyperplane separating the two classes and

the closest points to it, deﬁned as the support vectors.

In linearly separable data, the kernel of SVM used is

the maximal margin classiﬁer or hard margin SVM

(Vapnik, 1999).

Random Forest classiﬁers are based on the Deci-

sion Tree algorithm. Decision Trees are a supervised

method of classiﬁcation in machine learning, using

pre-classiﬁed data. The division of the data is based

on the values of features of the given data, by de-

ciding which features, best divide ir, creating a set

of rules for the values of each feature. The Random

Forest classiﬁer is a combination of multiple decision

trees, where each decision tree is made by randomly

selecting portions of the data, reducing the correla-

tion between trees, improving the prediction power

and results with a higher efﬁciency (Breiman, 2001;

Donges, 2018).

3 RESULTS

The results obtained showed that the PVSAT induced

stress to the subjects, reﬂected by the increase in heart

rate and in EDA features, such as SCR and SCL, dur-

ing stress.

Frequency analysis of EDA, also conﬁrmed the

activation of the sympathetic nervous system with an

increase in Band 1 power in stress.

For HRV, the results obtained for spectral mea-

sures were opposite to the expected. It was expected

to verify an increase in LF(nu) and LF/HF ratio dur-

ing stress, but no signiﬁcant result was found for

frequency-domain features.

Despite no signiﬁcance was obtained in

frequency-domain features for HRV, a thorough

analysis of these spectral characteristics, revealed

that in some subjects the LF(nu) decreased in stress,

while in other subjects there was an increase in

stress. Actually, within the 15 subjects that were

analyzed, there was a division of 8 subjects in which

LF(nu) decreased during stress, and 7 subjects that

LF(nu) increased during stress. So, when analyzing

the group as a whole, it is possible that the opposite

responses cancels out the LF(nu) results. Therefore,

two distinct groups were formed: group 1 consisted

of subjects which LF(nu) decreased during stress,

and group 2 consisted of subjects which LF(nu)

increased during stress. Then, all features for HRV

were analyzed for each group.

For group 1, the results showed a signiﬁcant in-

crease in HF(nu) power, LF(nu) power and LF/HF ra-

tio. For group 2, the results showed signiﬁcant effect

only for Bpm and RR interval .

3.1 Support Vector Machines

In this section, SVM were applied to try separate by a

hyperplane the two different responses of the subjects

to stress: decrease in LF(nu) and increase in LF(nu)

(see Fig.9). This separation is based on the work of

Vuksanovic et al. (Vuksanovi

c and Gal, 2007), that

veriﬁed this distinct response to stress, but in respect

to HF power.

First, a binary classiﬁcation of each group was ap-

plied: Group 1 - Decrease in LF(nu) was classiﬁed as

Y = −1 and Group 2 - Increase in LF(nu) was classi-

ﬁed as Y = 1. The decision function obtained to sep-

arate the two groups is given by equation 2, where

and w

represents, respectively, the weights for

groups 1 and 2, ~x

and ~x

represents, respectively, a

point for group 1 (Blue circles in Fig.9) and group 2

(Red circles in Fig.9).

.~x

+ w

.~x

+ b = 0 (2)

The results obtained for the weights and the b

parameter were: w

= −0.31, w

= 0.25 and b =

4.85. The number of support vectors for each

group were: Group 1 - 1 support vector, Group

2 - 2 support vectors. The coordinates ([LF(nu)

Baseline, LF(nu) Stress]) of the support vectors

(Black not ﬁlled circles in Fig.9) for each group

were: Group 1 - [78.48,74.64] and in Group 2 -

[77.35,81.25];[63.08,63.41].

Figure 9: SVM Group Separation by the hyperplane:

−0.31.~x

+0.25.~x

+4.85 = 0. Blue circles - Group 1. Red

circles - Group 2. The support vectors are the points with

black border.

Heart Rate Variability and Electrodermal Activity in Mental Stress Aloud: Predicting the Outcome

3.2 Linear Regression

In session 3.1, the results showed that the responses of

the groups were parallel, so it was possible to predict

the LF (nu) values during stress based on the base-

line values for each group separately. A linear regres-

sion was then computed for each group (Fig.10). For

Group 1 regression (Red line in Fig.10), the following

regression line was obtained: LF(nu) Stress = 1.40 ×

LF(nu) Baseline − 53.15,r

= 0.728. For Group 2 re-

gression (Blue line in Fig.10), the regression line ob-

tained was LF(nu) Stress = 1.06 × LF(nu) Baseline +

1.48,r

= 0.972.

Finally, a chi-squared test for goodness of ﬁt was

applied to the regression lines, comparing the ex-

pected values with the observed values using the re-

gression line obtained. For group 1 the chi-square re-

sult was χ

(6) = 12.785; p = 0.047 and for group 2

was χ

(5) = 0.674; p = 0.984. With the results ob-

tained for the χ

statistic, it is possible to reject at a

signiﬁcance level of 5%, the null hypothesis for group

1, concluding that the ﬁt of the regression line is not

adequate, while for group 2, with a p-value=0.984 it

is possible to accept the null hypothesis, concluding

that the ﬁt of the regression line is suitable.

Figure 10: Linear Regression for each group. Group

1 regression line (Red line): LF(nu) Stress = 1.40 ×

LF(nu) Baseline − 53.15, r

= 0.728; Group 2 regression

line (Blue line): LF(nu) Stress = 1.06 × LF(nu) Baseline +

1.48,r

= 0.972.

3.3 Random Forest Classiﬁer

In section 3.1, it was possible to separate the subjects

into two groups, by evaluating their response to stress,

with an increase or a decrease in LF(nu) during stress.

As this separation is based on a frequency-domain

feature, requiring the recording of the data for at least

10 minutes (5-min in baseline, 5-min in stress), in or-

der to predict the subject’s response to a situation of

stress in a shorter recording time, a classiﬁcation of

the subjects using only time-domain features for both

HRV and EDA, was performed, to classify the sub-

jects into the two different groups obtained in the pre-

vious section.

This classiﬁcation was performed with a random

forest classiﬁer, with 10 decision trees, and a Gini

criteria to assess the impurity and the quality of the

split. Training of the classiﬁer was performed with

a cross validation method, using 6 different random

splits and a test sample of 30% of the subjects. This

process was repeated 100 times, so that it was possi-

ble to choose the model that best classiﬁes the data,

that is, the model with a higher accuracy score for the

cross validation training method.

First, a random forest classiﬁer using only the

following time-domain features for HRV was per-

formed: Bpm, RR-interval and SD2/SD1 ratio. Then,

the importance of each feature is plotted in ﬁgure 11.

From this ﬁgure, we verify that RR-interval is the

most important feature in this model, followed by the

SD2/SD1 ratio and the Bpm. The accuracy score for

this model to classify correctly each subject to the cor-

responding group was approximately 80%.

Figure 11: Feature Importance for HRV features obtained

with the random forest classiﬁer.

In order to obtain a better visualization of the re-

gions deﬁned by the random forest classiﬁer, the fea-

tures boundaries are shown in a 3D graph (Fig. 12).

A subject with features coordinates that belong to the

blue region will be assigned to group 1 - decrease in

LF(nu), and subjects that belong to the red region will

be assigned to group 2 - Increase in LF(nu).

Information related to EDA was added to the classi-

ﬁer. Similarly to the previous classiﬁer, in order to

reduce the recording time, only time-domain features

for EDA were added to the classiﬁer. The following

features for EDA were selected: SCR, SCL and Rise

Time. The more accurate estimators were selected,

with the corresponding decision trees. The impor-

tance of each feature in plotted in ﬁgure 13. The ac-

curacy score for this model was approximately 77%.

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

Figure 12: 3D Decision Surface for the Random Forest

Classiﬁer. Features selected: Bpm, SD2/SD1 and RR-

interval (s). Blue region - Group 1 and Red region - Group

Figure 13: Feature Importance for HRV and EDA features

obtained with the random forest classiﬁer.

4 DISCUSSION

This was a pilot study to see the inﬂuence of stress

induction on the autonomous nervous system, by pro-

cessing HRV and EDA from a wearable device.

The results obtained in section 3, showed that the

arithmetic test (PVSAT) induced stress to the sub-

jects, reﬂected by the increase in heart rate (Bpm)

and in EDA features, such as, SCR and SCL, during

stress.

For EDA features, SCR, SCL, Rise time, Rec.t

50% and Rec.t 63%, revealed to be good markers of

stress, with the increase of values during all the seg-

ments studied during stress compared to the baseline

segments. In section 2.4.5, the frequency analysis of

EDA signals was performed to conﬁrm the activation

of the sympathetic nervous system with an increase

in power for low frequency bands. The results ob-

tained showed that there was a signiﬁcant increase in

Band 1 power. This conﬁrms that the dynamics of

the sympathetic nervous system are conﬁned to low

frequencies, in agreement with the work performed

by Posada et al., although in this paper the frequency

band studied was extended more 0.10 Hz, the increase

in power was also veriﬁed, making frequency analysis

of EDA a potential marker of quantitative assessment

of the level of stress and sympathetic nervous system

impairments (Posada-Quintero et al., 2016a).

For HRV, the results obtained for spectral mea-

sures were opposite to the expected. The inducement

of stress in subjects was expected to increase LF(nu)

and LF/HF ratio (Visnovcova et al., 2013; Hjortskov

et al., 2004; Vuksanovi

c and Gal, 2007). Contrarily

to the expectation, the results obtained showed that

there was a decrease in LF(nu) and LF/HF ratio dur-

ing stress, results also reported by (Tharion et al.,

2009; Vuksanovi

c and Gal, 2007; Hjortskov et al.,

2004). Vuksanovic et al. reported that vocalization

of the answers, assigned to parasympathetic activity,

during the PVSAT interfered with the spectral anal-

ysis and concealed out the changes in spectral mea-

sures of HRV (Vuksanovi

c and Gal, 2007). Lange-

witz et al. showed that the breathing pattern for some

subjects during vocalization affects the low frequency

band power, as the breathing frequency falls in the

0.1 Hz frequency band, the resonance phenomenon

will not increase the power in the LF band (Lange-

witz and Ruddel, 1989), concluding that the fact sub-

jects answered the PVSAT aloud might have inﬂu-

enced the spectral measures of HRV. These facts also

show that the LF band does not reﬂect purely the car-

diac response to the activation of the sympathetic ner-

vous system, but a mixture of the sympathetic and

parasympathetic systems, with counteracting effects

of activation of the sympathetic system and with-

drawal of the parasympathetic system (Sloan et al.,

1991). From the point of view of humoral mecha-

nisms, these results can be explained, as, during a

situation of stress, the sympathetic nervous system

affects the heart through release of catecholamines

(Terkelsen et al., 2005), such as epinephrine, lead-

ing to an increase in heart rate without changing heart

rate variability measures, as the release of epinephrine

does not affect spectral measures (Ahmed et al.,

1994).

In section 3.1, despite the results for HRV were

concealed out when analyzing the subjects as a whole,

it was possible to verify signiﬁcant changes in spec-

tral measures for HRV after separating the subjects

into the two different groups, based on the work per-

formed by Vuksanovic et al., and as an exploratory

method in order to ﬁnd a pattern, taking into account

that subjects can exhibit distinct response when sub-

mitted to stress. From ﬁgure 10, it is possible to see

that the slopes for each group do not intercept with

one another, so the two responses are parallel. For

group 2, the results obtained were in agreement with

Heart Rate Variability and Electrodermal Activity in Mental Stress Aloud: Predicting the Outcome

the expectations that during stress, the LF(nu) and

the LF/HF ratio increased with a small decrease in

HF(nu) power with no signiﬁcance. This group re-

sponds to stress with the withdrawal of the parasym-

pathetic nervous system and the activation of the sym-

pathetic nervous system. For group 1, the results

showed signiﬁcant decrease in LF(nu) and LF/HF ra-

tio during stress and signiﬁcant increase in HF(nu)

power. The simultaneous increase in HF(nu) and

heart rate is more difﬁcult to explain, although it could

be an inﬂuence of complex respiratory pattern (Vuk-

sanovi

c and Gal, 2007), or it could be the effect of

different co-activation humoral mechanims, caused

by compensatory sympatho-adrenal activation with

catecholamine release into the circulation (Terkelsen

et al., 2005).

In terms of EDA, both groups showed an increase

in Band 1 power, although signiﬁcance was only

found in group 2 between baseline 2 and stress 1 seg-

ments. It is possible to conclude that even if there is

a distinct response to stress in terms of HRV, there is

activation of the sympathetic nervous system during

the stress situation, due to the fact that the sympa-

thetic nervous system inﬂuences the heart and sweat

through distinct hormones, respectively, epinephrine

and acetylcholine.

Finally, the classiﬁcation model implemented in

section 3.3, showed that it was possible to predict the

type of response for each subject during stress, using

only their baseline features for both HRV and EDA

features, making it possible to classify the subjects

into the two different groups, with an accuracy of ap-

proximately 80% for HRV features in baseline and an

accuracy of approximately 77% for HRV and EDA

simultaneous features. This model assumes to be a

good asset for future assessment of the type of re-

sponse when the subjects are under a stress situation.

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