Background Endoscopic retrograde cholangiopancreatography (ERCP) requires moderate to deep sedation, usually with propofol. Adverse effects of propofol sedation are relatively common, such as respiratory and cardiovascular depression. This study was conducted to determine if doxapram, a respiratory stimulant, could be used to reduce the incidence of respiratory depression. Methods This is a single-center, prospective randomized double-blind study performed in the endoscopy unit of Helsinki University Central Hospital. 56 patients were randomized in a 1:1 ratio to either receive doxapram as an initial 1 mg/kg bolus and an infusion of 1 mg/kg/h (group DOX) or placebo (group P) during propofol sedation for ERCP. Main outcome measures were apneic episodes and hypoxemia (SpO 2 < 90%). Mann-Whitney test for continuous variables and Fisher's exact test for discrete variables were used and mixed effects modeling to take into account repeated measurements on the same subject and comparing both changes within a group as a function of time and between the groups. Results There were no statistically significant differences in apneic episodes (p = 0.18) or hypoxemia (p = 0.53) between the groups. There was a statistically significant rise in etCO 2 levels in both groups, but the rise was smaller in group P. There was a statistically significant rise in Bispectral Index (p = 0.002) but not modified Observer's Assessment of Agitation/Sedation (p = 0.21) in group P. There were no statistically significant differences in any other measured parameters. Conclusions Doxapram was not effective in reducing respiratory depression caused by deep propofol sedation during ERCP. Further studies are warranted using different sedation protocols and dosing regimens. Clinical trial registration ClinicalTrials.gov ID NCT02171910.
Objective. Electroencephalogram (EEG) recordings often contain large segments with missing signals due to poor electrode contact or other artifact contamination. Recovering missing values, contaminated segments and lost channels could be highly beneficial, especially for automatic classification algorithms, such as machine/deep learning models, whose performance relies heavily on high-quality data. The current study proposes a new method for recovering missing segments in EEG. Approach. In the proposed method, the reconstructed segment is estimated by substitution of the missing part of the signal with the normalized weighted sum of other channels. The weighting process is based on inter-channel correlation of the non-missing preceding and proceeding temporal windows. The algorithm was designed to be computationally efficient. Experimental data from patients (N = 20) undergoing general anesthesia due to elective surgery were used for the validation of the algorithm. The data were recorded using a portable EEG device with ten channels and a self-adhesive frontal electrode during induction of anesthesia with propofol from waking state until burst suppression level, containing lots of variation in both amplitude and frequency properties. The proposed imputation technique was compared with another simple-structure technique. Distance correlation (DC) was used as a measure of comparison evaluation. Main results.:The proposed method with average distance correlation of 82.48±10.01 (µ ± σ)% outperformed its competitor with average distance correlation of 67.89±14.12 (µ ± σ)% . This algorithm also showed better performance for an increasing number of missing channels. Significance. the proposed technique provides an easy-to-implement and computationally efficient approach for the reliable reconstruction of missing or contaminated EEG segments.
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