Intraoperative awareness during general anesthesia
Conditions
Interventions
Trade Name: Propofol – Lipuro 10 mg/ml
Product Name: Propofol
Pharmaceutical Form: Injection
Sponsors
None listed
Eligibility
Sex/Gender
All
Inclusion criteria
Inclusion criteria: -18-65 years old -right-handed -normal or corrected to normal vision -normal or corrected to normal hearing Are the trial subjects under 18? no Number of subjects for this age range: F.1.2 Adults (18-64 years) yes F.1.2.1 Number of subjects for this age range 12 F.1.3 Elderly (>=65 years) no F.1.3.1 Number of subjects for this age range
Exclusion criteria
Exclusion criteria: -neurological impairment -motor disabilities -known allergies or oversensitivity to propofol -allergies to peanuts and/or soy -cardiac respiratory, renal or hepatic impairment -regular drug intake (of any kind, other than contraceptives and antihistamines) -pregnancy or nursing
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Main Objective: By recording the EEG from participants before, during and after administration of low-dose propofol, it can be tested whether our system is able to reliably detect movements in the presence of low-dose anesthetic drugs. Thus, analyses should reveal whether under such conditions a clinically feasible true positive response can be obtained, while at the same time maintaining a clinically feasible false positive rate.;Secondary Objective: The brain responses between different conditions (absence and presence of low-dose anesthetic drugs) can be compared, possibly leading to new insights regarding the nature and robustness of movement-related EEG- changes.;Primary end point(s): The main study parameter is the classification rate of our algorithm, i.e. the percentage of movement trials that are correctly classified as movement trials and the percentage of non-movement trials that are correctly classified as non-movement trials, as well as its underlying TPR/FPR tradeoff. Although for some applications speed may be prioritized over accuracy, for the current application it is important that false alarms are kept to an absolute minimum. By plotting a receiver operating characteristic (ROC) curve for the classifier output, it can be determined whether our required maximum FPR and minimum TPR can be obtained. From these calculations the time needed for a correct detection can also be determined. Although the algorithm is programmed in such a way that it can take into account any feature from the EEG that distinguishes between the two classes, we expect the main useful feature to be the combined Event-Related Desynchronization (ERD), a power decrease in the a- and ß-frequency bands known to occur during (planning) of movement and Event-Related Synchronization (ERS), a power increase in approximately the same frequencies, known to occur after movement has stopped. These features have been well established in the literature and these findings have been replicated | — |
Secondary
| Measure | Time frame |
|---|---|
| Secondary end point(s): Not applicable;Timepoint(s) of evaluation of this end point: Not applicable | — |
Countries
Netherlands
Outcome results
None listed