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: tianhonger0@163.com

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

Copyright © 2013 Honger Tian et al. This is an open access article distributed under the Creative Commons Attribution License,

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

dance of the Creative Commons Attribution License all Copyrights © 2013 are reserved for SCIRP and the owner of the intellectual

property Honger Tian et al. All Copyright © 2013 are guarded by law and by SCIRP as a guardian.

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

1

12 1

2

,log

xX

px

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

12 21

1

12

2

,

,,

log

xX

Jpp

Ipp Ipp

px

px pxpx

(2)

Distance change in the of two probability distributions

can meet the metric nature. The distance change was

Copyright © 2013 SciRes. OPEN A CCESS