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: Contribution to journalArticlepeer-review

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.

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Analysis of the automatic detection of critical epochs from coma-EEG by dominant components and features extraction.'. Together they form a unique fingerprint.

Cite this