You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact

A personalised case-based stress diagnosis system using physiological sensor signals


Research group:

Publication Type:

Doctoral Thesis


Mälardalen University


Stress is an increasing problem in our present world. It is recognised that increased exposure to stress may cause serious health problems if undiagnosed and untreated. In stress medicine, clinicians’ measure blood pressure, Electrocardiogram (ECG), finger temperature and respiration rate etc. during a number of exercises to diagnose stress-related disorders. However, in practice, it is difficult and tedious for a clinician to understand, interpret and analyze complex, lengthy sequential sensor signals. There are few experts who are able to diagnose and predict stress-related problems. Therefore, a system that can help clinicians in diagnosing stress is important. This research work has investigated Artificial Intelligence techniques for developing an intelligent, integrated sensor system to establish diagnosis and treatment plans in the psychophysiological domain. This research uses physiological parameters i.e., finger temperature (FT) and heart rate variability (HRV) for quantifying stress levels. Large individual variations in physiological parameters are one reason why case-based reasoning is applied as a core technique to facilitate experience reuse by retrieving previous similar cases. Feature extraction methods to represent important features of original signals for case indexing are investigated. Furthermore, fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness and uncertainty inherently existing in clinicians’ reasoning.The evaluation of the approach is based on close collaboration with experts and measurements of FT and HRV from ECG data. The approach has been evaluated with clinicians and trial measurements on subjects (24+46 persons). An expert has ranked and estimated the similarity for all the subjects during classification. The result shows that the system reaches a level of performance close to an expert in both the cases. The proposed system could be used as an expert for a less experienced clinician or as a second opinion for an experienced clinician to supplement their decision making tasks in stress diagnosis.


author = {Shahina Begum},
title = {A personalised case-based stress diagnosis system using physiological sensor signals },
number = {103},
month = {June},
year = {2011},
school = {M{\"a}lardalen University},
url = {}