Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia
- Publikationstyp:
- Zeitschriftenaufsatz
- Metadaten:
-
- Autoren
- Moritz Tacke
- Eberhard F Kochs
- Marianne Mueller
- Stefan Kramer
- Denis Jordan
- Gerhard Schneider
- DOI
- 10.1371/journal.pone.0238249
- Editoren
- Gabor Erdoes
- eISSN
- 1932-6203
- Ausgabe der Veröffentlichung
- 8
- Zeitschrift
- PLOS ONE
- Sprache
- en
- Online publication date
- 2020
- Paginierung
- e0238249 - e0238249
- Status
- Published online
- Herausgeber
- Public Library of Science (PLoS)
- Herausgeber URL
- http://dx.doi.org/10.1371/journal.pone.0238249
- Datum der Datenerfassung
- 2020
- Titel
- Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia
- Ausgabe der Zeitschrift
- 15
Datenquelle: Crossref
- Andere Metadatenquellen:
-
- Abstract
- Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are applied to construct an index which predicts responsiveness in anesthetized patients. The present analysis considers several classification algorithms, among those support vector machines, artificial neural networks and Bayesian learning algorithms. On the basis of data from the transition between consciousness and unconsciousness, a combination of EEG and AEP signal parameters developed with automated methods provides a maximum prediction probability of 0.935, which is higher than 0.916 (for EEG parameters) and 0.880 (for AEP parameters) using a cross-validation approach. This suggests that machine learning techniques can successfully be applied to develop an improved combined EEG and AEP parameter to separate consciousness from unconsciousness.
- Autoren
- M Tacke
- EF Kochs
- M Mueller
- S Kramer
- D Jordan
- G Schneider
- Autoren-URL
- http://dx.doi.org/10.1371/journal.pone.0238249
- DOI
- 10.1371/journal.pone.0238249
- ISSN
- 1932-6203
- Ausgabe der Veröffentlichung
- 8
- Zeitschrift
- PLoS One
- Notes
- Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are applied to construct an index which predicts responsiveness in anesthetized patients. The present analysis considers several classification algorithms, among those support vector machines, artificial neural networks and Bayesian learning algorithms. On the basis of data from the transition between consciousness and unconsciousness, a combination of EEG and AEP signal parameters developed with automated methods provides a maximum prediction probability of 0.935, which is higher than 0.916 (for EEG parameters) and 0.880 (for AEP parameters) using a cross-validation approach. This suggests that machine learning techniques can successfully be applied to develop an improved combined EEG and AEP parameter to separate consciousness from unconsciousness.
- Paginierung
- e0238249
- Datum der Veröffentlichung
- 2020
- Herausgeber
- Public Library of Science (PLoS)
- Datum der Datenerfassung
- 2024
- Titel
- Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia
- Sub types
- JOUR
- Ausgabe der Zeitschrift
- 15
Datenquelle: Manual
- Beziehungen:
- Eigentum von