The complexity of occupational stress electroencephalogram

doi:10.4236/odem.2013.11001

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Vol.1, No.1, 1-3 (2013) Occupational Diseases and Environmental Medicine

http://dx.doi.org/10.4236/odem.2013.11001

The complexity of occupational stress

electroencephalogram

Honger Tian1, Lili Cao1, Jun Wang2*, Tian Xu1, Yongguo Zhan1, Ling Liu1

1Key Laboratory of Environment Medicine and Engineering, Ministry of Education, School of Public Health, Southeast University,

Nanjing, China

2Image Processing and Image Communications Key Lab, College of Geo & Bio Information, Nanjing University of Posts & Tele-

comm, Nanjing, China; *Corresponding Author: [email protected]

Received 18 September 2013; revised 20 October 2013; accepted 4 November 2013

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accor-

ABSTRACT

It is an important method for using electroen-

cephalogram (EEG) to detect and diagnose oc-

cupational Stress in clinical practice. In this pa-

per, the complexity analysis method based on

Jensen-Shannon Divergence was used to cal-

culate the complexity of occupational stress

electroencephalogram from students and nurses.

The study found that the complexity of nurses’

EEG was higher than that of students’ EEG. The

result can be used to assisted clinical diagnosis.

Keywords: Occupational Stress;

Electroencephalogram; Student s;

Nurses; Jensen-Shannon Divergence

1. INTRODUCTION

Since Jensen-Shannon Divergence (JSD) [1] (which

was used to measure the difference between the prob-

ability distribution of random variables) was proposed in

1991, it was widely applied to the symbol sequence

analysis and characterization [2], such as pattern recog-

nition [3], DNA sequence segmentation. JSD is the result

of symmetrizing and smoothing the Kullback-Leibler

Divergence (KLD). The non-negativity, symmetry, con-

tinuity [4,5] and boundness features of JSD have been

widely used in the analysis of time series. Electroen-

cephalogram (EEG) can also be seen as a time series, so

we consider using JSD complexity based analysis me-

thod to achieve recognition and detection of EEG. In this

paper, 12 graduate students from Southeast University

and 12 nurses from a third-grade class-A hospital

were chosen for comparative analysis.

Nurses need to withstand pressure from work, patient

and family. The occupational stress can cause changes in

psychology, physiology and behavior, such as anxiety,

irritability, depression, chronic fatigue syndrome, sleep

disorders, immune system suppression, cardiovascular

system disease, aggression, and bad habits [6-9]. Gradu-

ate students in universities relatively have lower occupa-

tional stress than the nurses. So their EEG should have a

different dynamic complexity. The Jensen-Shannon Di-

vergence was used to quantitatively analyze time series.

As a statistic parameter in information statistics, Jensen-

Shannon Divergence described the complexity between

different signals and the entropy value increased with

increased complexity.

2. JENSEN-SHANNON DIVERGENCE

(JSD)

Proposed that 1, 2 were two probability distribu-

tion of discrete random variables X. KULLBACK de-

fined direct difference I as:

p p

 



12 1

,log

Ipppx px



 (1)

It can be seen from Equation (1) that differences I has

non-negativity and incremental feature, but it does not

have symmetry. In order to meet this point, the form of a

symmetric difference—J difference was proposed:







 



12 21

log

Jpp

Ipp Ipp

px pxpx









(2)

Distance change in the of two probability distributions

can meet the metric nature. The distance change was

H. E. Tian et al. / Occupational Diseases and Environmental Medicine 1 (2013) 1-3

defined as follows:

 

121 2

Vpppxp x





 (3)

For the relationship between the direct differences I

and distance changes V, it was found the minimum value

of the differences I based on the V:

 





12112 212

,max,, ,IppLVpp LVpp (4)

where,











 

112

12 12

2,2,

log, 0,2

2,2,

LVpp

Vpp VppVpp

Vpp Vpp





 

(5)











 

12 12

212

,236

,,0, 2

288

Vpp Vpp

LVpp

Vpp Vpp





(6)

But there are not a general representation of the max-

imum value for direct differences I and differences J

based on V. So there exists some limitations when meas-

uring the difference between the probability distribution.

For generally descripting the maximum limit of the

difference, papers [1,9] gave the amended definition of I

and J:

 

 

12 1

11 2

,log

,22

Kpppx px px

Ip pp

















(7)

The corresponding symmetrical form difference was

as follows:



1212 21

,,LppKppKp p



(8)

It can be seen from Equation (8) that, the difference

between K and L was not only meeting the non-negativ-

ity but also having limitation and semi-bounded. That is:

 

1212 11

,,, ,

ppKpp Kpp

LppLpp Lpp

 

  (9)

  

121 2

,2 2

LppHHpHp









 (10)

Now setting 1



and 2



were the weight of two

probability distributions, and meeting

121 2

,0, 1





, Jensen-Shannon Divergence

(JSD) was defined as:

 





1211221122

SppH ppHpHp



 

 

(11)

where



Ppp



 is Shannon Entropy.

For more than two but a limited number of probability

distributions , the corresponding weight

were

,,,

pp p

..., n12

, ,





respectively, JSD was defined as:



,,,

niii



SpppHpHp

















 (12)

3. DATA ANALYSIS

The EEG data we used were taken from 12 students

and 12 nurses. The sampling frequency was 200 Hz. We

used JSD to analyze the complexity of the EEG data of

12 students and 12 nurses and the corresponding results

were shown in Table 1.

According to Table 1, we can plot the complexity

measures of three ECG signals and twelve were shown in

Figure 1.

It can be seen from the Figure 1 that, the Jensen-

Shannon Divergence value of students is less than that of

nurses. The T test value of the two groups was equal to

4.414 which confidence probability is 0.001. It indicated

that high level of occupational stress had a higher Jensen-

Shannon Divergence value than low level, and EEG

should be more complex. So students and nurses can be

statistically distinguished.

4. CONCLUSION

In this paper, the complexity analysis method based on

Jensen-Shannon Divergence was used to calculate the

complexity of occupational stress electroencephalogram

Table 1. Complexity measures of twelve ECG signals (2000

points).

Subjects Student Nurse

1 0.2163 0.2215

2 0.2175 0.2787

3 0.0599 0.2813

4 0.2092 0.2403

5 0.1929 0.2659

6 0.0694 0.2793

7 0.2171 0.2727

8 0.2358 0.2809

9 0.0692 0.2714

10 0.2145 0.2707

11 0.2055 0.2798

12 0.0621 0.2743

mean ± STD 0.1641 ± 0.0738 0.2681 ± 0.0184

H. E. Tian et al. / Occupational Diseases and Environmental Medicine 1 (2013) 1-3

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Student Nurse

0.1

0.2

0.3

0.4

0.5

Complexity

Student

Nurse

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Figure 1. Dynamic range of two kind signals’

complexity (N = 2000) (mean ± STD). ○ (Student),

* (Nurse).

from students and nurses. The study found that the com-

plexity of nurses’ EEG was higher than that of students’

EEG. The result can be used to assisted clinical diagno-

sis.

[6] Bagaajav, A., Myagmarjav, S., Nanjid, K., Otgon, S. and

Chae, Y.M. (2011) Burnout and job stress among Mon-

golian doctors and nurses. Industrial Health, 49, 582-588.

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depressive state in nurses. Occupational Medicine, 60,

231-233. http://dx.doi.org/10.1093/occmed/kqp167

5. ACKNOWLEDGEMENTS

This work was supported by the National Natural Science Founda-

tion of China (Grant Nos. 61271082, 61201029, 61102094), the Natural

Science Foundation of Jiangsu Province (Grant Nos. BK2011759,

BK2011565), the Social development Foundation-science and technol-

ogy support projects of Jiangsu province (Grant Nos. BE2011777), and

Foundation of Nanjing University of Posts and Telecommunications

(JG03212JX02, JG03210JX19, 2011XSG11).

[8] V

oltmer, E., Wingenfeld, K., Spahn, C., Driessen, M. and

Schulz, M. (2012) Work-related behaviour and ex-

perience patterns of nurses in different professional stages

and settings compared to physicians in Germany. Inter-

national Journal of Mental Health Nursing, 22, 180-189.

http://dx.doi.org/10.1111/j.1447-0349.2012.00855.x

[9] Kikuchi, Y., Nakaya, M., Ikeda, M., Takeda, M. and Nishi,

M. (2013) Job stress and temperaments in female nurses.

Occupational Medicine, 63, 123-128.

http://dx.doi.org/10.1093/occmed/kqs212

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