Anaesthesia Monitoring by Recurrence Quantification Analysis of EEG Data

被引：32

作者：

Becker, Klaus ^{[1
]}

Schneider, Gerhard ^{[2
]}

Eder, Matthias ^{[3
]}

Ranft, Andreas ^{[2
]}

Kochs, Eberhard F. ^{[2
]}

Zieglgaensberger, Walter ^{[3
]}

Dodt, Hans-Ulrich ^{[1
]}

机构：

[1] Vienna Univ Technol, A-1040 Vienna, Austria

[2] Tech Univ Munich, Klinikum Rechts Isar, Dept Anaesthesiol, D-80804 Munich, Germany

[3] Max Planck Inst Psychiat, Neuronal Network Dynam, D-80804 Munich, Germany

来源：

PLOS ONE | 2010年 / 5卷 / 01期

关键词：

SURGICAL-PATIENTS; BISPECTRAL INDEX; WAKEFULNESS; AWARENESS; PLOTS; STATE;

D O I：

10.1371/journal.pone.0008876

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Appropriate monitoring of the depth of anaesthesia is crucial to prevent deleterious effects of insufficient anaesthesia on surgical patients. Since cardiovascular parameters and motor response testing may fail to display awareness during surgery, attempts are made to utilise alterations in brain activity as reliable markers of the anaesthetic state. Here we present a novel, promising approach for anaesthesia monitoring, basing on recurrence quantification analysis (RQA) of EEG recordings. This nonlinear time series analysis technique separates consciousness from unconsciousness during both remifentanil/sevoflurane and remifentanil/propofol anaesthesia with an overall prediction probability of more than 85%, when applied to spontaneous one-channel EEG activity in surgical patients.

引用

页数：6

共 20 条

[11] Narcotrend® does not adequately detect the transition between awareness and unconsciousness in surgical patients [J].