Photoplethysmography-derived approximate entropy and sample entropy as measures of analgesia depth during propofol-remifentanil anesthesia

Neonatal Outcomes in Labor After Intravenous Remifentanil Analgesia vs. Epidural Analgesia: A Retrospective Observational Study

Background Some evidence indicates that maternal analgesia during labor may have adverse effects on neonates due to exposure to specific drugs or the potential effects of analgesia on the course of labor. We assessed the clinical outcome of term neonates born to mothers who received epidural analgesia (E) or systemic analgesia with remifentanil (R) during labor. Methods Data was collected retrospectively over one year. We have evaluated the medical records of 247 full-term neonates; 208 were born to mothers who received E and 39 to mothers who received R. Data on Apgar scores and neonatal complications (perinatal asphyxia, respiratory distress, infection, hyperbilirubinemia, and birth injuries), and average hospital stay were collected. Mann-Whitney U test, chi-square test, and logistic regression analysis were used where appropriate. Results The values of the mean Apgar scores between E and R at 1 and 5 minutes were similar (8.83 vs. 8.97, p = 0.252; 9.81 vs. 9.87, p = 0.762, respectively). The average length of neonatal hospitalization did not differ between groups (4.19 vs. 4; p = 0.557). The percentages of neonates with any complication were similar between groups (28.3% vs. 32.5%, p = 0.598). Neonates born by cesarean delivery (CD) had significantly worse outcomes than those born vaginally (p = 0.008, OR 2.8, 95% CI [1.30, 6.17]). Conclusion We did not find a statistically significant difference in mean Apgar scores and neonatal complications between neonates who received epidural vs. remifentanil analgesia. An increased rate of complications in neonates born via CD was found. Future studies should have a larger sample size and be powered to detect associations in these findings.

The sex effect on balance control while standing on vestibular-demanding tasks with/without vestibular simulations: implication for sensorimotor training for future space missions

Background: Anatomical differences between sexes in the vestibular system have been reported. It has also been demonstrated that there is a sex difference in balance control while standing on vestibular-demanding tasks. In 2024, NASA expects to send the first female to the Moon. Therefore, to extend the current knowledge, this study attempted to examine whether different sexes respond differently to vestibular-disrupted and vestibular-demanding environments. Method: A total of fifteen males and fifteen females participated in this study. The vestibular function was quantified through different SOT conditions (SOT1: baseline; SOT5: vestibular demanding by standing with blindfolded and sway reference surface). The vestibular stimulation (VS) was applied either unilaterally or bilaterally to vestibular system to induce the sensory-conflicted and challenging tasks. Thus, a total of 6 conditions (2 SOT conditions X 3 VSs: no-VS, unilateral VS, and bilateral VS) were randomly given to these participants. Three approaches can be quantified the balance control: 1) the performance ratio (PR) of center of gravity trajectories (CoG), 2) the sample entropy measure (SampEn) of CoG, and 3) the total traveling distance of CoG. A mixed three-way repeated ANOVA measure was used to determine the interaction among the sex effect, the effect of SOT, and the effect of VS on balance control. Results: A significant sex effect on balance control was found in the PR of CoG in the anterior-posterior (AP) direction (p = 0.026) and in the SampEn of CoG in both AP and medial-lateral (ML) directions (p = 0.025, p < 0.001, respectively). Also, a significant interaction among the sex effect, the effect of SOT, and the effect of VS on balance control was observed in PR of CoG in the ML direction (p < 0.001), SampEn of CoG in the AP and ML directions (p = 0.002, p < 0.001, respectively), and a traveling distance in AP direction (p = 0.041). Conclusion: The findings in the present study clearly revealed the necessity to take sex effect into consideration while standing in vestibular-perturbed or/and vestibular demanding tasks. Also, the results in the present study could be a fundamental reference for future sensorimotor training.

Analysis of the Chaotic Component of Photoplethysmography and Its Association with Hemodynamic Parameters

Wearable technologies face challenges due to signal instability, hindering their usage. Thus, it is crucial to comprehend the connection between dynamic patterns in photoplethysmography (PPG) signals and cardiovascular health. In our study, we collected 401 multimodal recordings from two public databases, evaluating hemodynamic conditions like blood pressure (BP), cardiac output (CO), vascular compliance (C), and peripheral resistance (R). Using irregular-resampling auto-spectral analysis (IRASA), we quantified chaotic components in PPG signals and employed different methods to measure the fractal dimension (FD) and entropy. Our findings revealed that in surgery patients, the power of chaotic components increased with vascular stiffness. As the intensity of CO fluctuations increased, there was a notable strengthening in the correlation between most complexity measures of PPG and these parameters. Interestingly, some conventional morphological features displayed a significant decrease in correlation, indicating a shift from a static to dynamic scenario. Healthy subjects exhibited a higher percentage of chaotic components, and the correlation between complexity measures and hemodynamics in this group tended to be more pronounced. Causal analysis showed that hemodynamic fluctuations are main influencers for FD changes, with observed feedback in most cases. In conclusion, understanding chaotic patterns in PPG signals is vital for assessing cardiovascular health, especially in individuals with unstable hemodynamics or during ambulatory testing. These insights can help overcome the challenges faced by wearable technologies and enhance their usage in real-world scenarios.

Low-intensity transcranial ultrasound stimulation modulates neural activities in mice under propofol anaesthesia

BACKGROUND

Previous studies have reported that transcranial focused ultrasound stimulation can significantly decrease the time to emergence from intraperitoneal ketamine-xylazine anaesthesia in rats. However, how transcranial focused ultrasound stimulation modulates neural activity in anaesthetized rats is unclear.

METHODS

In this study, to answer this question, we used low-intensity transcranial ultrasound stimulation (TUS) to stimulate the brain tissue of propofol-anaesthetized mice, recorded local field potentials (LFPs) in the mouse motor cortex and electromyography (EMG) signals from the mouse neck, and analysed the emergence and recovery time, mean absolute power, relative power and entropy of local field potentials.

RESULTS

We found that the time to emergence from anaesthesia in the TUS group (20.3 ± 1.7 min) was significantly less than that in the Sham group (32 ± 2.6 min). We also found that compared with the Sham group, 20 min after low-intensity TUS during recovery from anaesthesia, (1) the absolute power of local field potentials in mice was significantly reduced in the [1-4 Hz] and [13-30 Hz] frequency bands and significantly increased in the [55-100 Hz], [100-140 Hz] and [140-200 Hz] frequency bands; (2) the relative power of local field potentials in mice was enhanced at [30-45 Hz], [100-140 Hz] and [140-200 Hz] frequency bands; (3) the entropy of local field potentials ([1-200 Hz]) was increased.

CONCLUSION

These results demonstrate that low-intensity TUS can effectively modulate neural activities in both awake and anaesthetized mice and has a positive effect on recovery from propofol anaesthesia in mice.

The efficacy and safety of remifentanil patient-controlled versus epidural analgesia in labor: A meta-analysis and systematic review

BACKGROUND

Remifentanil patient-controlled analgesia (rPCA) and epidural analgesia (EA) has been used for pain relief in labor. We aimed to evaluate the efficacy and safety of rPCA versus EA in labor, to provide evidence support for clinical analgesia and pain care.

METHODS

We searched PubMed, EMBASE, ScienceDirect, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang and Weipu databases for RCTs comparing rPCA and EA in labor until February 15, 2022. Two researchers independently screened literature and extracted data. RevMan 5.3 software was used for data analysis.

RESULTS

A total of 10 RCTs involving 3086 parturients were enrolled, 1549 parturients received rPCA and 1537 received EA. Meta-analysis indicated that the incidence of intrapartum maternal fever within 1 hour of labor analgesia (OR = 0.43, 95%CI: 0.30~0.62), after 1 hour of labor analgesia (OR = 0.42, 95%CI: 0.20~0.90) in the rPCA was significantly less than that of EA (all P<0.05). The incidence of respiratory depression (OR = 3.56, 95%CI: 2.45~5.16, P<0.001) in the rPCA was significantly higher than that of EA. There were no significant differences in the incidence of Apgar scores<7 at 5 minutes (OR = 1.18, 95%CI: 0.71~1.96, P = 0.53), the patients' satisfaction of pain relief during labor analgesia (SMD = 0.03, 95%CI: -0.40~0.46, P = 0.90) between rPCA and EA (all P>0.05).

CONCLUSION

rPCA can be an optional alternative to EA with similar pain relief and less risk of intrapartum maternal fever. However, rPCA was associated with increased risk of respiratory depression. Future studies with rigorous design and larger sample size are needed to provide more reliable evidences for clinical rPCA and EA use.

Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection

Anesthesia drug overdose hazards and lack of gold standards in anesthesia monitoring lead to an urgent need for accurate anesthesia drug detection. To investigate the PPG waveform features affected by anesthesia drugs and develop a machine-learning classifier with high anesthesia drug sensitivity. This study used 64 anesthesia and non-anesthesia patient data (32 cases each), extracted from Queensland and MIMIC-II databases, respectively. The key waveform features (total area, rising time, width 75%, 50%, and 25%) were extracted from 16,310 signal recordings (5-s duration). Discriminant analysis, support vector machine (SVM), and K-nearest neighbor (KNN) were evaluated by splitting the dataset into halve training (11 patients, 8570 segments) and halve testing dataset (11 patients, 7740 segments). Significant differences exist between PPG waveform features of anesthesia and non-anesthesia groups (p < 0.05) except total area feature (p > 0.05). The KNN classifier achieved 91.7% (AUC = 0.95) anesthesia detection accuracy with the highest sensitivity (0.88) and specificity (0.90) as compared to other classifiers. Kohen’s kappa also shows almost perfect agreement (0.79) with the KNN classifier. The KNN classifier trained with significant PPG features has the potential to be used as a reliable, non-invasive, and low-cost method for the detection of anesthesia drugs for depth analysis during surgical operations and postoperative monitoring.

Cerebral Tissue Oxygen Saturation Correlates with Emergence from Propofol-Remifentanil Anesthesia: An Observational Cohort Study

Anesthesia emergence is accompanied by changes in cerebral circulation. It is unknown whether cerebral tissue oxygen saturation (SctO₂) could be an indicator of emergence. Changes in SctO₂, bispectral index (BIS), mean arterial pressure (MAP), and heart rate (HR) were evaluated during the emergence from propofol-remifentanil anesthesia. At the time of cessation of anesthetic delivery, SctO₂, BIS, MAP, and HR values were recorded as baseline. The changes of these parameters from the baseline were recorded as Δ SctO₂, Δ BIS, Δ MAP, and Δ HR. The behavioral signs (body movement, coughing, or eye opening) and response to commands (indicating regaining of consciousness) were used to define emergence states. Prediction probability (Pk) was used to examine the accuracy of SctO₂, BIS, MAP, and HR as indicators of emergence. SctO₂ showed an abrupt and distinctive increase when appearing behavioral signs. BIS, MAP, and HR, also increased but with a large inter-individual variability. Pk value of Δ SctO₂ was 0.97 to predict the appearance behavioral signs from 2 min before that, which was much higher than the Pk values of Δ BIS (0.81), Δ MAP (0.71) and Δ HR (0.87). The regaining of consciousness was associated with a further increase in the SctO₂ value.

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.