Detection of EEG changes via a generalized Itakura distance

Xuan Kong, Xuesong Lou, Nitish V. Thakor

Research output: Contribution to journalConference articlepeer-review

Abstract

Changes in electroencephalogram (EEG) carry important clinical information. Accurate detection and characterization of such changes in EEG can be a valuable tool for clinical assessment of the neurological system condition. Autoregressive mode have previously been used to detect changes in the EEG signal. Based on the AR model parameters, an off-line distance measure called Itakura distance has been used to effectively quantify changes in the EEG signal related to brain injury. The ordinary Itakura distance measure used for such quantification requires the same order for the AR models in all EEG segment. In this paper, a generalized Itakura distance measure is proposed without the constraint of same order for all EEG segments. The generalized Itakura distance measure is applied to the analysis of EEG signals. Preliminary results suggest that the generalized Itakura distance measure performs better for detecting injury-related changes in EEG.

Original languageEnglish (US)
Pages (from-to)1540-1542
Number of pages3
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume4
StatePublished - Dec 1 1997
Externally publishedYes
EventProceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, USA
Duration: Oct 30 1997Nov 2 1997

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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