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 Mälardalen 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

Licentiate proposal seminar: Artificial intelligence diagnostics in psychophysiological medicine


Markus Nilsson , Markus Nilsson



Start time:

2003-09-23 13:30

End time:

2003-09-23 14:30



Contact person:


Candidate: Markus Nilsson, Department of Computer Science and Engineering, MdHAdvisory Committee: Main advisor: docent Peter Funk, IDt, MdH Assistant advisor: professor Björn Lisper, IDt, Mdh Assistant advisor: dr Bo von Schéele, National Institute for Psychosocial Medicine Karolinska Institutet Independent third person: professor Ylva Bäcklund, IEl, MdHAbstractThe contents of the licentiate proposal is concerning the use of AI in classification of complex measurements. Measurements has previously only been classifiable by domain specialists due to the complexity of the measurements. A method has been developed to accomplish this classification. The method is mainly based on CBR but uses a number of other techniques. Both AI and mathematical methods is used for feature identification. The system is classifying continuous non-stationary measurements, and has the capacity to improve performance with a growing number of solved cases. A system based on the proposed method is implemented as a clinical application (in psychphysiological medicine) as a proof of concept and in evaluation purposes.

Markus Nilsson,