Real-Time EEG Signal Classification for Monitoring and Predicting the Transition between Different Anaesthetic States

Tai Nguyen-Ky, Hoang Duong Tuan, Andrey Savkin, Minh N. Do, Nguyen Thi Thu Van

Research output: Contribution to journalArticlepeer-review

Abstract

Quantitative identification of the transitions between anaesthetic states is very essential for optimizing patient safety and quality care during surgery but poses a very challenging task. The state-of-the-art monitors are still not capable of providing their manifest variables, so the practitioners must diagnose them based on their own experience. The present paper proposes a novel real-time method to identify these transitions. Firstly, the Hurst method is used to pre-process the de-noised electro-encephalograph (EEG) signals. The maximum of Hurst's ranges is then accepted as the EEG real-time response, which induces a new real-time feature under moving average framework. Its maximum power spectral density is found to be very differentiated into the distinct transitions of anaesthetic states and thus can be used as the quantitative index for their identification.

Original languageEnglish (US)
Article number9329155
Pages (from-to)1450-1458
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume68
Issue number5
DOIs
StatePublished - May 2021

Keywords

  • Depth of anaesthesia (DoA)
  • electro-encephalograph (EEG)
  • moving average
  • power spectral density

ASJC Scopus subject areas

  • Biomedical Engineering

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