Analysis of the automatic detection of critical epochs from coma-EEG by dominant components and features extraction

Giuseppina Inuso, Fabio La Foresta, Nadia Mammone, F. Carlo Morabito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent works showed that meaningful dominant components can be extracted from the EEG of patients in coma through an algorithm based on the joint use of Principal Component Analysis (PCA) and Independent Component Analysis (ICA). A procedure for automatic critical epoch detection would support the doctor in the long time monitoring of the patients, thus we investigated the automatic quantification of the criticality of the epochs. In this paper we propose a procedure based on the extraction of dominant components and features for the quantification of the critical state of each epoch, in particular we use entropy and kurtosis. This feature analysis allowed us to detect some epochs that are likely to be critical and that are worth being carefully inspected electrographically by the expert.

Original languageEnglish
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Pages5727-5730
Number of pages4
DOIs
Publication statusPublished - 2006
Event28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States
Duration: Aug 30 2006Sep 3 2006

Other

Other28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
CountryUnited States
CityNew York, NY
Period8/30/069/3/06

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ASJC Scopus subject areas

  • Bioengineering

Cite this

Inuso, G., La Foresta, F., Mammone, N., & Morabito, F. C. (2006). Analysis of the automatic detection of critical epochs from coma-EEG by dominant components and features extraction. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (pp. 5727-5730). [4029945] https://doi.org/10.1109/IEMBS.2006.259505