Support-vector classification of low-dose nitrous oxide administration with multi-channel EEG power spectra.

Autor: Vrijdag XCE; Department of Anaesthesiology, School of Medicine, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand. x.vrijdag@auckland.ac.nz., Hallum LE; Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland, 1142, New Zealand., Tonks EI; Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland, 1142, New Zealand., van Waart H; Department of Anaesthesiology, School of Medicine, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand., Mitchell SJ; Department of Anaesthesiology, School of Medicine, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand.; Department of Anaesthesia, Auckland City Hospital, Auckland, 1023, New Zealand., Sleigh JW; Department of Anaesthesiology, School of Medicine, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand.; Department of Anaesthesia, Waikato Hospital, Hamilton, 3240, New Zealand.
Jazyk: angličtina
Zdroj: Journal of clinical monitoring and computing [J Clin Monit Comput] 2024 Apr; Vol. 38 (2), pp. 363-371. Date of Electronic Publication: 2023 Jul 13.
DOI: 10.1007/s10877-023-01054-w
Abstrakt: Support-vector machines (SVMs) can potentially improve patient monitoring during nitrous oxide anaesthesia. By elucidating the effects of low-dose nitrous oxide on the power spectra of multi-channel EEG recordings, we quantified the degree to which these effects generalise across participants. In this single-blind, cross-over study, 32-channel EEG was recorded from 12 healthy participants exposed to 0, 20, 30 and 40% end-tidal nitrous oxide. Features of the delta-, theta-, alpha- and beta-band power were used within a 12-fold, participant-wise cross-validation framework to train and test two SVMs: (1) binary SVM classifying EEG during 0 or 40% exposure (chance = 50%); (2) multi-class SVM classifying EEG during 0, 20, 30 or 40% exposure (chance = 25%). Both the binary (accuracy 92%) and the multi-class (accuracy 52%) SVMs classified EEG recordings at rates significantly better than chance (p < 0.001 and p = 0.01, respectively). To determine the relative importance of frequency band features for classification accuracy, we systematically removed features before re-training and re-testing the SVMs. This showed the relative importance of decreased delta power and the frontal region. SVM classification identified that the most important effects of nitrous oxide were found in the delta band in the frontal electrodes that was consistent between participants. Furthermore, support-vector classification of nitrous oxide dosage is a promising method that might be used to improve patient monitoring during nitrous oxide anaesthesia.
(© 2023. The Author(s).)
Databáze: MEDLINE
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