Physiological and Psychological Responses to Coding Combined with Horticultural Activity

This study was conducted to determine the physiological and psychological benefits of integrating software coding and horticultural activity. Participants included 30 adults in their 20s. The subjects randomly engaged in activities—namely, connecting Arduino components, coding, planting, and a combined coding and horticultural activities. During the activity, two subjective evaluations were conducted at the end of each activity, and participants’ brain waves were measured. The spectral edge frequency 50% of alpha spectrum band (ASEF50) and ratio of sensorimotor rhythm from mid beta to theta (RSMT) were activated in the prefrontal lobe as participants performed combined coding and horticultural activities. When performing these combined activities, relative beta (RB) increased, and relative theta (RT) decreased in the prefrontal lobe. In addition, ASEF50, relative low beta (RLB), and relative mid beta (RMB) were activated during plant-based activities (planting and a combined coding and horticultural activities). The subjective evaluations revealed that the plant-based activities had a positive effect on participants’ emotions. This study shows that activities combining coding and horticulture had a positive impact on physiological relaxation and increased concentration in adults compared with other activities and was also linked with positive subjectively reported emotions.

Effects of the Essential Oils of Sweet Orange, Lavender and Amyris on EEG Activity

Purpose: This study evaluated the effects of the mixed essential oils containing sweet orange, lavender, and amyris (MEO) on human electroencephalogram (EEG) activity.Methods: EEG activity was recorded by examining the sequence of brain waves of 20 adults, aged from 20 to 30, before and during inhaling the mixed essential oils.Results: MEO showed activity centered on the frontal lobe, which is responsible for higher-order functions against external stimuli, and this result indicated that the oils acted as an intellectual effect. Additional experiments showed that the brain was relaxed and stabilized through a decrease in the absolute slow alpha (ASA) and the relative slow alpha (RSA), a decrease in the absolute beta (AB) and the absolute high beta (AHB), and an increase in the spectral edge frequency 50% of alpha (ASEF), respectively. Also, the oils induced the awakening states of the brain with a decrease in the absolute alpha (AA) and the absolute theta (AT), and increase of the spectral edge frequency 50% (SEF50). Furthermore, it was possible to confirm the state of brain immersion through the increase in the absolute fast alpha (AFA), relative fast alpha (RFA), relative mid beta (RMB), ratio of mid beta to theta (RMT), ratio of SMR to theta (RSMT), relative gamma (RG) and the spectral edge frequency 90% (SEF90).Conclusion: Taken together, these results suggest that the inhaling of MEO affect the brain to be a good condition and improves its concentration ability.

Electroencephalogram variability analysis for monitoring depth of anesthesia

In this paper, a new approach of extracting and measuring the variability in electroencephalogram (EEG) was proposed to assess the depth of anesthesia (DOA) under general anesthesia. The EEG variability (EEGV) was extracted as a fluctuation in time interval that occurs between two local maxima of EEG. Eight parameters related to EEGV were measured in time and frequency domains, and compared with state-of-the-art DOA estimation parameters, including sample entropy, permutation entropy, median frequency and spectral edge frequency of EEG. The area under the receiver-operator characteristics curve (AUC) and Pearson correlation coefficient were used to validate its performance on 56 patients. Our proposed EEGV-derived parameters yield significant difference for discriminating between awake and anesthesia stages at a significance level of 0.05, as well as improvement in AUC and correlation coefficient on average, which surpasses the conventional features of EEG in detection accuracy of unconscious state and tracking the level of consciousness. To sum up, EEGV analysis provides a new perspective in quantifying EEG and corresponding parameters are powerful and promising for monitoring DOA under clinical situations.

The Effect of Different Concentrations of Halothane Anaesthesia on the Electroencephalograph of Rock Doves (Columba livia)

Anaesthetic agents and doses used can significantly impact cerebrocortical responsiveness as assessed by electroencephalography (EEG). The objective of this study was to evaluate the effect of three different halothane concentrations on the EEG of Rock Doves using measures of frequency distribution and burst suppression. Eight healthy Rock Doves (Columba livia) were anaesthetized with halothane in oxygen, their tracheas intubated and their lungs mechanically ventilated. Five minutes of EEG were recorded at three multiples of minimum anaesthetic concentration (MAC), 1× MAC (1.6%), 1.5× MAC (2.4%) and 2× MAC (3.2%), presented in ascending then descending order. Fast Fourier transformation of the raw EEG record gave the median frequency (F50), spectral edge frequency (F95) and the total power (Ptot). Burst suppression, expressed as inactive compared to active EEG (%), was calculated on a representative two-minute section of the raw EEG. Data were analysed using repeated-measures one-way ANOVA with Tukey post hoc correction for comparison of 1×, 1.5× and 2× MAC. Three of eight birds demonstrated negligible (<1%) burst suppression. No effect of halothane concentration on burst suppression incidence was seen. A significant decrease in all measured frequency variables (F50, p = 0.04; F95p = 0.02; Ptotp < 0.0001) occurred between 1× and 2× MAC. Halothane anaesthesia at MAC multiples of 1×, 1.5× and 2× in the Rock Dove can be considered suitable where cortical responsiveness is desired.

A retrospective study of electroencephalogram parameters during general anaesthesia in infants and young children

Background This retrospective study describes electroencephalogram (EEG) parameters in infants and young children under general anaesthesia. The study’s primary objective was to detect determinants of patient state index (PSI) levels. Methods We analysed EEG parameters in patients aged 1–36 months who received sevoflurane or propofol as the primary anaesthetic in Beijing Children’s Hospital from September 1, 2019, to April 30, 2020. Patients were stratified into two age groups: 1–12 and 13–36 months. The relationship between EEG parameters and clinical parameters was analysed by multivariable linear regression. Results Sixty-two patients were involved in the final analysis, including 35 boys and 27 girls. Average patient age and weight were 14.5 (5.8–24.0) months and 9.9 (8.0–12) kg, respectively. Spectral edge frequency (SEF), PSI and blood pressure were lower (p < 0.001) and burst suppression rate (BSR) and heart rate higher (p < 0.001) at the age of 1–12 months under general anaesthesia. The most significant factor associated with higher levels of PSI was a higher SEF at the age of 1–36 months (1–12 months p = 0.001; 13–26 months p < 0.001); burst suppression occurrence decreased the PSI level (p = 0.031). Older age or higher blood pressure increased PSI levels in children under 1 year old (p = 0.028 and p = 0.002). Conclusions SEF was the most significant determinant associated with PSI levels in infants and young children during general anaesthesia, and burst suppression occurrence decreases the PSI level. Moreover, age and blood pressure might affect PSI levels in infants.

Proposed EEG measures of consciousness: a systematic, comparative review.

Knowledge of which brain properties are required for consciousness is essential for improving clinical diagnostics and therapy as well as for investigating consciousness per se. The search for such properties has yielded many methods and measures for distinguishing conscious and apparently unconscious brain states. Here, we present a systematic literature review of 255 electroencephalography (EEG)-based measures of consciousness in humans. We show that measures based on signal diversity and event related potentials appear to be the most consistent. Specifically, spectral entropy, Lempel Ziv complexity, and spectral edge frequency, seem most practical, consistent, and reproducible. However, since most studies did not collect current or retrospective subjective reports about experiences, the states of consciousness were usually assessed from behavior. Hence, the ground truth about presence or absence of phenomenological experience in such cases is often uncertain, thus limiting the conclusions that can be drawn. We provide detailed overviews of general categories and specific measures of consciousness, to serve as a basis for further studies and future development.

The influence of induction speed on the frontal (processed) EEG

The intravenous injection of the anaesthetic propofol is clinical routine to induce loss of responsiveness (LOR). However, there are only a few studies investigating the influence of the injection rate on the frontal electroencephalogram (EEG) during LOR. Therefore, we focused on changes of the frontal EEG especially during this period. We included 18 patients which were randomly assigned to a slow or fast induction group and recorded the frontal EEG. Based on this data, we calculated the power spectral density, the band powers and band ratios. To analyse the behaviour of processed EEG parameters we calculated the beta ratio, the spectral entropy, and the spectral edge frequency. Due to the prolonged induction period in the slow injection group we were able to distinguish loss of responsiveness to verbal command (LOvR) from loss of responsiveness to painful stimulus (LOpR) whereas in the fast induction group we could not. At LOpR, we observed a higher relative alpha and beta power in the slow induction group while the relative power in the delta range was lower than in the fast induction group. When concentrating on the slow induction group the increase in relative alpha power pre-LOpR and even before LOvR indicated that frontal EEG patterns, which have been suggested as an indicator of unconsciousness, can develop before LOR. Further, LOvR was best reflected by an increase of the alpha to delta ratio, and LOpR was indicated by a decrease of the beta to alpha ratio. These findings highlight the different spectral properties of the EEG at various levels of responsiveness and underline the influence of the propofol injection rate on the frontal EEG during induction of general anesthesia.

Preliminary Study of the Use of Root with Sedline® EEG Monitoring for Assessment of Anesthesia Depth in 6 Horses

Management of equine anesthesia monitoring is still a challenge. Careful monitoring to provide guidelines for anesthesia depth assessment currently relies upon eye signs, cardiopulmonary responses, and the level of muscle relaxation. Electroencephalography, as a non-invasive brain activity monitor, may be used to complement the routinely monitored physiologic parameters. Six horses, undergoing various surgical procedures and anesthesia protocols, were monitored with the use of a Root with Sedline EEG monitor and a routine monitor of life parameters. The life parameters were compared to the changes on the EEG density spectral array observed live during anesthesia. During all procedures the level of awareness was monitored using the EEG, with higher frequency and power of waves indicating a higher level of awareness. It was evident from this that there were variations according to the type of procedure and the anesthetic protocol. Cerebral activity was elevated during painful moments of the surgery and recovery, requiring adjustments in anesthetic concentrations. Evaluation of changes in the spectral edge frequency (SEF) could show the periods when the patient is stabilized. EEG monitoring has the potential to be used in clinical anesthesiology of horses. It was shown that this system may be used in horses under general anesthesia but is currently less effective in a standing horse for diagnostic or minor procedures.

Data Driven Investigation of Bispectral Index Algorithm

Bispectral index (BIS), a useful marker of anaesthetic depth, is calculated by a statistical multivariate model using nonlinear functions of electroencephalography-based subparameters. However, only a portion of the proprietary algorithm has been identified. We investigated the BIS algorithm using clinical big data and machine learning techniques. Retrospective data from 5,427 patients who underwent BIS monitoring during general anaesthesia were used, of which 80% and 20% were used as training datasets and test datasets, respectively. A histogram of data points was plotted to define five BIS ranges representing the depth of anaesthesia. Decision tree analysis was performed to determine the electroencephalography subparameters and their thresholds for classifying five BIS ranges. Random sample consensus regression analyses were performed using the subparameters to derive multiple linear regression models of BIS calculation in five BIS ranges. The performance of the decision tree and regression models was externally validated with positive predictive value and median absolute error, respectively. A four-level depth decision tree was built with four subparameters such as burst suppression ratio, power of electromyogram, 95% spectral edge frequency, and relative beta ratio. Positive predictive values were 100%, 80%, 80%, 85% and 89% in the order of increasing BIS in the five BIS ranges. The average of median absolute errors of regression models was 4.1 as BIS value. A data driven BIS calculation algorithm using multiple electroencephalography subparameters with different weights depending on BIS ranges has been proposed. The results may help the anaesthesiologists interpret the erroneous BIS values observed during clinical practice.