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

Clinical decision support by time series classification using wavelets



Fixed some typos in the CRC.

Publication Type:

Conference/Workshop Paper


Proceedings of the Seventh International Conference on Enterprise Information Systems (ICEIS05)




Clinicians do sometimes need help with diagnoses, or simply need reinsurance that they make the right decision. This could be provided to the clinician in the form of a decision support system. We have designed and implemented a decision support system for the classification of time series. The system is called HR3Modul and is designed to assist clinicians in the diagnosis of respiratory sinus arrhythmia. Two parallel streams of physiological time series are analysed for the classification task. Patterns are retrieved from one of the time series by the support of the other time series. These patterns are transformed with wavelets and matched for similarity by Case-Based Reasoning. Pre-classified patterns are stored and are used as knowledge in the system. The amount of patterns that have to be matched for similarity is reduced by a clustering technique. In this paper, we show that classification of physiological time series by wavelets is a viable option for clinical decision support.


author = {Markus Nilsson and Peter Funk and Ning Xiong},
title = {Clinical decision support by time series classification using wavelets},
note = {Fixed some typos in the CRC.},
editor = {Chin-Sheng Chen, Joaquim Filipe, Isabel Seruca, Jos{\'e} Cordeiro},
pages = {169--175},
month = {May},
year = {2005},
booktitle = {Proceedings of the Seventh International Conference on Enterprise Information Systems (ICEIS05)},
publisher = {INSTICC Press},
url = {}