Relationship between Depression Level and Bio-signals by Emotional

Stimuli

Eun-Hye Jang, Ah-Young Kim, Sang-Hyeob Kim and Han-Young Yu

Bio-Medical IT Convergence Research Department, Electronics and Telecommunications Research Institute,

Gajeongno, Yuseong-gu, Daejeon, Republic of Korea

Keywords: Depression, Bio-Signal, Emotion.

Abstract: Recent studies in mental/physical health monitoring have noted to improve health and wellbeing with the

help of Information and Communication Technology (ICT) and in particular, application of biosensors has

mainly done because signal acquisition by non-invasive sensors is relatively simple as well as bio-signal is

less sensitive to social/cultural difference. Prior to developing a depression monitoring system based on

non-invasive bio-signals, we examined a relationship of depressive level and changes of biological features

during exposure of emotional stimuli. Ninety-six subjects’ depressive level was measured by a self-rating

depression scale (SDS). Electrocardiogram (ECG) and photoplethysmograph (PPG) were recorded during

six baseline and emotional states (interest, joy, neutral, pain, sadness and surprise) and heart rate (HR) and

pulse transit time (PTT) were extracted. Pearson’s correlation was conducted to examine the relation of

depressive level and biological features. The results showed that relation of depressive level and HR is

positive in emotional states and there is a negative correlation between depressive level and PTT. We

identified that they are meaningful biological features related to depression.

1 INTRODUCTION

In the field of ICT for health and wellbeing, the most

current trends have shown that there have been

increasingly various studies on correlating mental

disorders to non-invasive biological measures such

as skin conductance response, heart rate, and

temperature. They have several advantages in that

signal acquisition by non-invasive sensors is

relatively simple as well as bio-signal is less

sensitive to social/cultural difference. Also, it is

known that several bio-signals are significantly

correlated with human emotional state (Drummond

and Quah, 2001; Tefas, Kotropoulos and Pitas,

2001). To provide more effective wellbeing service,

it is considered to understand and recognize the

emotions of humans. They mainly target depression

and bipolar disorder and make use of mobile

technologies and biosensors (Riva et al., 2011).

They rely on biosensor devices for monitoring

physiological parameters and on electronic self-

assessment of mood so that the early warning signs

of relapse into depression or bipolar disorder can be

better recognized (Warmerdam et al., 2012; Faurholt

-Jepsen, 2013; Valenza, Gentili, Lanat`a and

Scilingo, 2013). However, because there are

currently no objective biological markers by non-

invasive techniques used in the diagnosis of

depression, there has been a surge of research

activity that has shed light on both the

neurobiological, physiological, and behavioural

effects of depression (Sung, Marci and Pentland,

2005). In this study, to identify biological measures

related to depression as a preliminary study for a

depression monitoring system based on bio-signals,

we have considered heart rate (HR) and the

dynamics of the PTT time series as tools for a better

diagnostic of depression. Therefore, we examined a

relationship between depressive level and changes of

biological features, i.e., heart rate (HR) and pulse

transit time (PTT) during exposure of six emotional

stimuli (interest, joy, neutral, pain, sadness and

surprise).

2 EXPERIMENTAL METHODS

2.1 Participants

Ninety-six male and female college students (mean

138

Jang, E-H., Kim, A., Kim, S-H. and Yu, H-Y.

Relationship between Depression Level and Bio-signals by Emotional Stimuli.

DOI: 10.5220/0006005301380141

In Proceedings of the 3rd International Conference on Physiological Computing Systems (PhyCS 2016), pages 138-141

ISBN: 978-989-758-197-7

20.0 years ± 1.8) participated in this study. None of

them reported any history of medical illness of

taking psychotropic medication and any medication

that would affect the cardiovascular, respiratory, or

central nervous system. A written consent was

obtained at the beginning of the study when they

were introduced to the experimental procedures, and

they were also paid $30 USD to compensate for their

participation.

2.2 Emotional Stimuli

To successfully provoke target emotions, we have

used audio-visual clips as emotional stimuli. Each

emotional clip which was excerpted from a variety

of movies and pictures from PC etc. lasted 1- to 3-

minute long. They were counter-balanced to

minimize the order effect. Table 1 is the example of

the emotional stimuli to induce six emotions. The

emotional stimuli had 81.8% effectiveness on

average by the other experiment for collection of

emotional stimuli. The effectiveness means the

intensity of the induced emotion by each stimulus.

Table 1: Examples of emotional stimuli.

Emotion Context of Stimuli

Interest

Joy

Neutral

Pain

Sadness

Surprise

2.3 Experimental Procedures for the

Measures of Bio-Signals

Prior to measures of bio-signals, they rated their

depressive level on a self-rating depression scale

(SDS) by Zung (1965). Then, bio-signals were

recorded using MP150 (Biopac, Inc., USA) during

baseline and six emotional states (interest, joy,

neutral, pain, sadness and surprise). ECG was

recorded through Ag/AgCl surface electrodes from

the bilateral wrist and the left ankle as a reference.

PPG were detected at the volar surface of the distal

phalanx of the thumb of non-dominant hand.

For the data analysis, we chose the most

emotional 30-sec section from six emotional states.

HR and PTT were extracted from the signals. HR in

beats per minute was analysed by an AcqKnowledge

(version 3.7.1) program that detects R-waves in the

ECG and calculates consecutive R–R intervals. PTT

in ms was determined as the time between the R-

wave in the ECG and the systolic peak in the pulse

signal.

To analyse the biological data, we chose the

most stable 30-seconds section from the baseline and

the most emotional 30-seconds section from each

emotional states. The emotional states were

determined based on the results of the participant’s

self-reporting, in which an emotion was most

strongly expressed during the presentation of a

stimulus. Pearson’s correlation examined the

relation between depressive level and changes of

biological features induced by emotional stimuli.

3 RESULTS

3.1 Validity of Emotion Induction

The results of appropriateness and effectiveness by

the participants’ ratings mean their psychological

responses to emotional stimuli. The emotional

stimuli had 92.0% effectiveness on average. The

effectiveness of each emotional stimulus is as

follows: interest 88.0%, joy 81.6%, neutral 94.8%,

pain 95.9%, sadness 94.8% and surprise 97.0%).

3.2 Relationship between Depressive

Level and Biological Features

Pearson’s correlation coefficient (Pearson's r) as a

measure of the linear correlation between two

variables was used to examine the relation between

depressive level and biological features during

emotional states (Table 1). The results showed that

relation of depressive level and HR is significantly

positive in emotional states (Figure 1). This means

that depressive level is higher, HR increase.

Relationship between Depression Level and Bio-signals by Emotional Stimuli

139

Table 2: The results of Pearson’s r: correlation between

depressive level and biological features during baseline

and emotional states.

Emotional States

INT JOY NEU PAI SAD SUR

.136 .237* .274* .208* .339*** .178*

PTT -.300** -.167 -.308*** -.090 -.189 -.228**

*. p <.05, ** p < .01, *** p < .001

Figure 1: The relation between depressive level and heart

rate (HR) during six emotional states.

Figure 2: The relation between depressive level and pulse

wave transit time (PTT) during six emotional states.

In addition to, there was a negative correlation

between depressive level and PTT, i.e., depressive

level is higher, PTT is lower (Figure 2). In particular,

the correlation results of PPT were significant in

three emotional states, interest, neutral, and surprise.

4 CONCLUSIONS

We have attempted to investigate the relationship

between depressive level and changes of biological

features induced by six emotional stimuli. The

results showed that relation of depressive level and

HR, SCL are positive in all emotional states except

for interest and there was a negative correlation

between depressive level and PTT in three emotional

states. This means that depressive level is higher,

HR increases and depressive level is higher, PTT is

lower. In clinical study, increased heart rate was

reported in the depressed in spite of unchanged

autonomic balance (Moser et al., 1998). Also, they

have discussed the possibility that the increased

heart rate seen in the absence of vagal tone changes

may not be due to altered vagal or sympathetic tone

and other factors, including altered autonomous

heart rate, may be responsible for the higher heart

rate in the depressed group (Figure 3).

Figure 3: Possible physiological factors determining heart

rate (right) and findings of Moser et al. (1998) (left)

indicating, that increased autonomous heart rate is most

likely the reason for the significantly heart rate in

depressed subjects.

Pulse wave transit time (PTT) measures the time

it takes for a pulse pressure wave to travel from the

aortic valve to the periphery (Khandelwal, Sahni,

Kumar and Kumar, 2014). PTT is inversely

proportional to blood pressure, and the falls in blood

pressure which occur with inspiration correspond to

rises in PTT. Therefore, shortened PTT means rises

in blood pressure and quantitative measure of

inspiratory effort.

Although we haven’t clearly examined that the

relationship between depressive level and biological

PhyCS 2016 - 3rd International Conference on Physiological Computing Systems

140

feature (in particular, PTT) is linear under all

emotional states (Table 1), we will perform the

additional work to improve correlation between

them. Spearman or Kendall correlation coefficient, a

nonparametric measure of rank correlation, may be

more suitable.

Nevertheless, we could identify that HR and PTT

are meaningful biological features related to

depression using non-invasive biosensors. This

result will contribute to the use of integrative

approaches capable of assessing multiple biological

variables in developing the depression monitoring

system.

ACKNOWLEDGEMENTS

This work was supported by Institute for

Information & communications Technology

Promotion (IITP) grant funded by the Korea

government (MSIP) (No. B0132-15-1003).

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