The effects of pterygomaxillary disjunction in surgically assisted rapid maxillary expansion: A systematic review and meta-analysis.

OBJECTIVE: This study aims to assess the expansive effects of pterygomaxillary disjunction (PMD) in surgically assisted rapid maxillary expansion (SARME) surgery using a meta-analysis approach. MATERIALS AND METHODS: The study conducted a comprehensive literature search across five databases: PubMed, Scopus, Medline, Embase, and Cochrane, adhering to the PRISMA 2020 guidelines. Dental alterations were assessed using either cone-beam computed tomography (CBCT) or dental casts, while skeletal changes were exclusively measured from CBCT scans. We analysed the dentoskeletal changes between PMD +/- groups and conducted a within-group comparison. The primary focus of the results was on the mean differences observed in pre- and post-operative measurements. RESULTS: Dental expansion was larger in the PMD+ group but not statistically significant. Skeletal expansion showed a significantly larger expansion in the posterior region in the PMD+ group (P = .033). Without PMD, anterior palatal expansion was significantly larger (P = .03), and the buccal tipping of posterior teeth was also significantly larger (P = .011) to achieve acceptable dental expansion outcomes. CONCLUSIONS: Both PMD +/- groups of SARME surgery can achieve satisfactory dental expansion outcomes. However, bone expansion and tooth inclination are also important factors that influence orthodontic treatment and post-expansion stability. By reducing the bony resistance with PMD, larger posterior palatal expansion and more parallel bony expansion are observed. In contrast, without PMD, there is smaller palatal expansion and greater tooth inclination in the posterior region. This could potentially lead to compromised periodontal conditions following expansion.

Fractal Properties of Heart Rate Dynamics: A New Biomarker for Anesthesia-Biphasic Changes in General Anesthesia and Decrease in Spinal Anesthesia.

Processed electroencephalogram (EEG) has been considered a useful tool for measuring the depth of anesthesia (DOA). However, because of its inability to detect the activities of the brain stem and spinal cord responsible for most of the vital signs, a new biomarker for measuring the multidimensional activities of the central nervous system under anesthesia is required. Detrended fluctuation analysis (DFA) is a new technique for detecting the scaling properties of nonstationary heart rate (HR) behavior. This study investigated the changes in fractal properties of heart rate variability (HRV), a nonlinear analysis, under intravenous propofol, inhalational desflurane, and spinal anesthesia. We compared the DFA method with traditional spectral analysis to evaluate its potential as an alternative biomarker under different levels of anesthesia. Eighty patients receiving elective procedures were randomly allocated different anesthesia. HRV was measured with spectral analysis and DFA short-term (4-11 beats) scaling exponent (DFAα1). An increase in DFAα1 followed by a decrease at higher concentrations during propofol or desflurane anesthesia is observed. Spinal anesthesia decreased the DFAα1 and low-/high-frequency ratio (LF/HF ratio). DFAα1 of HRV is a sensitive and specific method for distinguishing changes from baseline to anesthesia state. The DFAα1 provides a potential real-time biomarker to measure HRV as one of the multiple dimensions of the DOA.

Comparison of Deep Learning Algorithms in Predicting Expert Assessments of Pain Scores during Surgical Operations Using Analgesia Nociception Index.

There are many surgical operations performed daily in operation rooms worldwide. Adequate anesthesia is needed during an operation. Besides hypnosis, adequate analgesia is critical to prevent autonomic reactions. Clinical experience and vital signs are usually used to adjust the dosage of analgesics. Analgesia nociception index (ANI), which ranges from 0 to 100, is derived from heart rate variability (HRV) via electrocardiogram (ECG) signals, for pain evaluation in a non-invasive manner. It represents parasympathetic activity. In this study, we compared the performance of multilayer perceptron (MLP) and long short-term memory (LSTM) algorithms in predicting expert assessment of pain score (EAPS) based on patient's HRV during surgery. The objective of this study was to analyze how deep learning models differed from the medical doctors' predictions of EAPS. As the input and output features of the deep learning models, the opposites of ANI and EAPS were used. This study included 80 patients who underwent operations at National Taiwan University Hospital. Using MLP and LSTM, a holdout method was first applied to 60 training patients, 10 validation patients, and 10 testing patients. As compared to the LSTM model, which had a testing mean absolute error (MAE) of 2.633 ± 0.542, the MLP model had a testing MAE of 2.490 ± 0.522, with a more appropriate shape of its prediction curves. The model based on MLP was selected as the best. Using MLP, a seven-fold cross validation method was then applied. The first fold had the lowest testing MAE of 2.460 ± 0.634, while the overall MAE for the seven-fold cross validation method was 2.848 ± 0.308. In conclusion, HRV analysis using MLP algorithm had a good correlation with EAPS; therefore, it can play role as a continuous monitor to predict intraoperative pain levels, to assist physicians in adjusting analgesic agent dosage. Further studies may consider obtaining more input features, such as photoplethysmography (PPG) and other kinds of continuous variable, to improve the prediction performance.

Dietary citrate supplementation enhances longevity, metabolic health, and memory performance through promoting ketogenesis.

Citrate is an essential substrate for energy metabolism that plays critical roles in regulating cell growth and survival. However, the action of citrate in regulating metabolism, cognition, and aging at the organismal level remains poorly understood. Here, we report that dietary supplementation with citrate significantly reduces energy status and extends lifespan in Drosophila melanogaster. Our genetic studies in fruit flies implicate a molecular mechanism associated with AMP-activated protein kinase (AMPK), target of rapamycin (TOR), and ketogenesis. Mice fed a high-fat diet that supplemented with citrate or the ketone body ß-hydroxybutyrate (ßOHB) also display improved metabolic health and memory. These results suggest that dietary citrate supplementation may prove to be a useful intervention in the future treatment of age-related dysfunction.

Electroencephalogram variability analysis for monitoring depth of anesthesia.

Objective. 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.Approach. 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.Main results. 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.Significance. 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.

Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition.

According to a recently conducted survey on surgical complication mortality rate, 47% of such cases are due to anesthetics overdose. This indicates that there is an urgent need to moderate the level of anesthesia. Recently deep learning (DL) methods have played a major role in estimating the depth of Anesthesia (DOA) of patients and has played an essential role in control anesthesia overdose. In this paper, Electroencephalography (EEG) signals have been used for the prediction of DOA. EEG signals are very complex signals which may require months of training and advanced signal processing techniques. It is a point of debate whether DL methods are an improvement over the already existing traditional EEG signal processing approaches. One of the DL algorithms is Convolutional neural network (CNN) which is very popular algorithm for object recognition and is widely growing its applications in processing hierarchy in the human visual system. In this paper, various decomposition methods have been used for extracting the features EEG signal. After acquiring the necessary signals values in image format, several CNN models have been deployed for classification of DOA depending upon their Bispectral Index (BIS) and the signal quality index (SQI). The EEG signals were converted into the frequency domain using and Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD). However, because of the inter mode mixing observed in EMD method; EEMD have been utilized for this study. The developed CNN models were used to predict the DOA based on the EEG spectrum images without the use of handcrafted features which provides intuitive mapping with high efficiency and reliability. The best trained model gives an accuracy of 83.2%. Hence, this provides further scope and research which can be carried out in the domain of visual mapping of DOA using EEG signals and DL methods.

Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder.

This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson's linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system-the systolic blood pressure (SBP) and diastolic blood pressures (DBP)-the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system-the systolic and diastolic pressures-the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal.

Comparison of the efficacy of supraglottic airway devices in low-risk adult patients: a network meta-analysis and systematic review.

Numerous supraglottic airway device (SADs) have been designed for adults; however, their relative efficacy, indicated by parameters such as adequacy of sealing, ease of application, and postinsertion complications, remains unclear. We conducted a systematic review and network meta-analysis to evaluate the efficacy of various SADs. We searched electronic databases for randomized controlled trials comparing at least two types of SADs published before December 2019. The primary outcomes were oropharyngeal leak pressure (OLP), risk of first-attempt insertion failure, and postoperative sore throat rate (POST). We included 108 studies (n = 10,645) comparing 17 types of SAD. The Proseal laryngeal mask airway (LMA), the I-gel supraglottic airway, the Supreme LMA, the Streamlined Liner of the Pharynx Airway, the SoftSeal, the Cobra Perilaryngeal Airway, the Air-Q, the Laryngeal Tube, the Laryngeal Tube Suction II, the Laryngeal Tube Suction Disposable, AuraGain, and Protector had significantly higher OLP (mean difference ranging from 3.98 to 9.18 cmH2O) compared with that of a classic LMA (C-LMA). The Protector exhibited the highest OLP and was ranked first. All SADs had a similar likelihood of first-attempt insertion failure and POST compared with the C-LMA. Our findings indicate that the Protector may be the best SAD because it has the highest OLP.Systematic review registration PROSPERO: CRD42017065273.

Deceleration capacity of heart rate variability as a predictor of sedation related hypotension.

High risk and geriatric patients are supposed to suffer higher risks of hypotension underwent painless endoscopic procedures. This study evaluated different biomarkers associated with hypotension in off-site patients and aimed to determine the most relevant risk factors in space and monitoring limited environment. The inclusions of this observational cohort study underwent complex endoscopic procedures were sedated with age-adjusted doses of target-controlled infusion of propofol. The following pre-sedative parameters were analysed: time domain, frequency domain, and Deceleration capacity (DC) of heart rate variability, estimated cardiac output data and the index of cardiac contractility from the cardiometer. Patients were divided into hypotension group (blood pressure < 90 mmHg or a > 35% decrease) and non-hypotension group according to peri-sedative blood pressure, regression analysis is used to examine the association between factors and hypotension. Total data from 178 patients (age range: 33-94 years) were analysed. Age was not significantly different between the hypotension and non-hypotension groups (p = 0.978). Among all the factors, DC was most associated with hypotension (p = 0.05), better than cardiometer, age, and ASA status. In conclusion, DC, which can be interpreted as the indicator of parasympathetic activity and was significantly and negatively correlated with sedation-related hypotension. Pre-sedative measuring DC from routine ECG monitoring is simple and cost-effective and should be added to haemodynamic monitoring in the endoscopic room.

Effects of dexmedetomidine on renal microcirculation in ischemia/reperfusion-induced acute kidney injury in rats.

Microcirculatory dysfunction plays a crucial role in renal ischemia/reperfusion (IR)-induced injury. Dexmedetomidine was reported to ameliorate IR-induced acute kidney injury. This study investigated the effects of dexmedetomidine on renal microcirculation after IR-induced acute kidney injury in rats. In total, 50 rats were randomly allocated to the following five groups (10 in each group): Sham, Control‒IR, Dex (dexmedetomidine) ‒Sham, Dex‒IR, and IR‒Dex group. The microcirculation parameters included total small vessel density, perfused small vessel density (PSVD), proportion of perfused small vessels, microvascular flow index, and tissue oxygen saturation (StO2) were recorded. The repeated measures analysis showed that PSVD on renal surface was higher in the Dex‒IR group than in the Control‒IR group (3.5 mm/mm2, 95% confidence interval [CI] 0.6 to 6.4 mm/mm2, P = 0.01). At 240 min, StO2 on renal surface was lower in the Control‒IR group than in the Sham group (- 7%, 95% CI - 13 to - 1%, P = 0.021), but StO2 did not differ significantly among the Sham, Dex‒IR, and IR‒Dex groups. Our results showed that pretreatment with dexmedetomidine improved renal microcirculation in rats with IR-induced acute kidney injury. However, the adverse effects of low mean arterial pressure and heart rate might offset the protective effect of dexmedetomidine on organ injury.

Nervous System Deletion of Mammalian INDY in Mice Mimics Dietary Restriction-Induced Memory Enhancement.

Reduced expression of the Indy (I'm Not Dead Yet) gene extends life span in Caenorhabditis elegans and Drosophila melanogaster and improves the metabolic heath of Mus musculus through inducing a physiological status akin to dietary restriction (DR). Although the function of Indy in aging and hepatic metabolism has been extensively studied, its role in the mouse nervous system remains unclear. Here, we explore the effect of mammalian Indy (mIndy, SLC13A5) gene deletion on murine cognitive function. Similar to what is seen in DR animals, systemic deletion of the mIndy gene (mIndy knockout [KO]) significantly improves memory performance and motor coordination of mice. Both DR and mIndy KO mice act normally in other behavioral tasks, including emotional, social, and food-seeking behaviors. Moreover, we find that tissue-specific deletion of mIndy in the nervous system is sufficient to improve memory performance, while liver-specific deletion has no effect on memory, and results in tests of motor coordination show no changes in either mutant. Mice with systemic or nervous system deletion of mIndy also exhibit increased hippocampal neurogenesis and dendritic spine formation in dentate granule cells; these changes are well-documented contributors to enhanced memory performance. Together, our studies demonstrate a critical role for brain-derived mIndy expression in the regulation of memory function in animals.

Comparing effects of intraoperative fluid and vasopressor infusion on intestinal microcirculation.

Several studies have revealed that vasopressor may be more appropriate for treating intraoperative hypotension and preventing hypervolemia. This study compared the effects of vasopressor infusion and fluid supplementation on intestinal microcirculation during treating intraoperative hypotension. Thirty-two rats were randomly divided into the following four groups: Light Anesthesia group (LA, 0.8-1% isoflurane); Deep Anesthesia group (DA, 1.5-1.8% isoflurane); Fluid DA group (1.5-1.8% isoflurane and fluid supplementation); and Norepinephrine DA group (1.5-1.8% isoflurane and norepinephrine infusion). At 240 min, perfused small vessel density (PSVD) of the mucosa did not differ significantly between the Fluid DA and Norepinephrine DA groups [26.2 (3.2) vs 28.9 (2.5) mm/mm2, P = 0.077], and tissue oxygen saturation of the mucosa was lower in the Fluid DA groups than in the Norepinephrine DA groups [ 48 (7) vs 57 (6) %, P = 0.02]. At 240 min, TSVD and PSVD of the seromuscular layer were higher in the Norepinephrine DA group than in the Fluid DA group. Fluid administration was higher in the Fluid DA group than in the Norepinephrine DA group [66 (25) vs. 9 (5) µL/g, P = 0.001]. Our results showed that norepinephrine can resuscitate intraoperative hypotension related microcirculatory alteration and avoid fluid overload.

Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography.

Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.

Multi-timescale phase-amplitude couplings in transitions of anesthetic-induced unconsciousness.

Under general anesthesia (GA), advanced analysis methods enhance the awareness of the electroencephalography (EEG) signature of transitions from consciousness to unconsciousness. For nonlinear and nonstationary signals, empirical mode decomposition (EMD) works as a dyadic filter bank to reserve local dynamical properties in decomposed components. Moreover, cross-frequency phase-amplitude coupling analysis illustrates that the coupling between the phase of low-frequency components and the amplitude of high-frequency components is correlated with the brain functions of sensory detection, working memory, consciousness, and attentional selection. To improve the functions of phase-amplitude coupling analysis, we utilized a multi-timescale approach based on EMD to assess changes in brain functions in anesthetic-induced unconsciousness using a measure of phase-amplitude coupling. Two groups of patients received two different anesthetic recipes (with or without ketamine) during the induction period of GA. Long-term (low-frequency) coupling represented a common transitional process of brain functions from consciousness to unconsciousness with a decay trend in both groups. By contrast, short-term coupling reflected a reverse trend to long-term coupling. However, the measures of short-term coupling also reflected a higher degree of coupling for the group with ketamine compared with that without ketamine. In addition, the coupling phase is a factor of interest. The phases for different combinations of coupling components showed significant changes in anesthetic-induced unconsciousness. The coupling between the delta-band phase and the theta-band amplitude changed from in-phase to out-phase coupling during the induction process from consciousness to unconsciousness. The changes in the coupling phase in EEG signals were abrupt and sensitive in anesthetic-induced unconsciousness.

Serotonin receptor HTR6-mediated mTORC1 signaling regulates dietary restriction-induced memory enhancement.

Dietary restriction (DR; sometimes called calorie restriction) has profound beneficial effects on physiological, psychological, and behavioral outcomes in animals and in humans. We have explored the molecular mechanism of DR-induced memory enhancement and demonstrate that dietary tryptophan-a precursor amino acid for serotonin biosynthesis in the brain-and serotonin receptor 5-hydroxytryptamine receptor 6 (HTR6) are crucial in mediating this process. We show that HTR6 inactivation diminishes DR-induced neurological alterations, including reduced dendritic complexity, increased spine density, and enhanced long-term potentiation (LTP) in hippocampal neurons. Moreover, we find that HTR6-mediated mechanistic target of rapamycin complex 1 (mTORC1) signaling is involved in DR-induced memory improvement. Our results suggest that the HTR6-mediated mTORC1 pathway may function as a nutrient sensor in hippocampal neurons to couple memory performance to dietary intake.

[Anesthetic Care of Children With Gene Therapy for the Treatment of Aromatic L-Amino Acid Decarboxylase (AADC) Deficiency].

AADC deficiency is a rare autosomal recessive disease that may lead to movement disorder and/or autonomic dysfunction. The prognosis of AADC deficiency patients is poor, and most die in childhood. Gene therapy is the only method currently available to help alleviate related symptoms. Gene therapy involves the injection of adeno-associated viral (AV) vector into the basal ganglia of patients, helping transfer the AADC gene and leading to improved AADC production and higher numbers of neurotransmitters in the brain. Moreover, as patients are unable to produce catecholamine, they may also suffer from a disorder affecting the regulatory control of the autonomic nervous system, resulting in hypoglycemia, which often causes imbalances in thermoregulation and hemodynamic and functional adjustments during surgery. Furthermore, the use of analgesics and inhalational anesthetics increase the risks of hypotension and bradycardia. Thus, in addition to assessing cardiac and respiratory system functions, it is important to evaluate the patient's airway before administering anesthesia, as structural anomalies or other situations may be present that lead to difficulties in tracheal intubation. Various airway tools, anesthesia equipment, and alternative plans should thus be prepared to protect the airway and maintain vital signs. All of the abovementioned issues increase the risks of AADC deficiency patients undergoing general anesthesia. The development of gene transfection for genetic diseases is a future trend. This paper identifies possible problems and related responses in perioperative patient care during gene therapy. The authors hope that these experiences provide references for the administration of AADC and similar gene therapies in the future.

The Performance of Supraglottic Airway Devices at Different Head and Neck Rotation Angles.

OBJECTIVE: Supraglottic airway devices (SGAs) have advantages in reducing respiration-associated complications. However, limited studies on the influence of head and neck rotation on the risk of air leakage have been conducted. We hypothesize that different head and neck rotation angles will increase the risk of air leakage when using SGAs. METHODS: A prospective, randomized study was conducted with 51 patients in the i-gelTM group and 50 patients in the AuraOnceTM group. The head and neck were subsequently rotated to the following positions: 0 (neutral), 15, 30, 45, and 60 degrees. Airway pressure over 20 cmH2O was defi ned as negative for air leakage. RESULTS: The percentage of air leakage was proportional to the increase in rotational degrees at the various rotation angles. The incidence of patients without air leakage at the largest angle (60 degrees) of head and neck rotation in both groups was approximately 80% (i-gelTM: 82.35%; AuraOnceTM: 79.59%).Conclusion: SGAs presented acceptable performance with increased head and neck rotation angles. CONCLUSION: SGAs presented acceptable performance with increased head and neck rotation angles.

Frontal EEG Temporal and Spectral Dynamics Similarity Analysis between Propofol and Desflurane Induced Anesthesia Using Hilbert-Huang Transform.

Electroencephalogram (EEG) signal analysis is commonly employed to extract information on the brain dynamics. It mainly targets brain status and communication, thus providing potential to trace differences in the brain's activity under different anesthetics. In this article, two kinds of gamma-amino butyric acid (type A -GABAA) dependent anesthetic agents, propofol and desflurane (28 and 23 patients), were studied and compared with respect to EEG spectrogram dynamics. Hilbert-Huang Transform (HHT) was employed to compute the time varying spectrum for different anesthetic levels in comparison with Fourier based method. Results show that the HHT method generates consistent band power (slow and alpha) dominance pattern as Fourier method does, but exhibits higher concentrated power distribution within each frequency band than the Fourier method during both drugs induced unconsciousness. HHT also finds slow and theta bands peak frequency with better convergence by standard deviation (propofol-slow: 0.46 to 0.24; theta: 1.42 to 0.79; desflurane-slow: 0.30 to 0.25; theta: 1.42 to 0.98) and a shift to relatively lower values for alpha band (propofol: 9.94 Hz to 10.33 Hz, desflurane 8.44 Hz to 8.84 Hz) than Fourier one. For different stage comparisons, although HHT shows significant alpha power increases during unconsciousness stage as the Fourier did previously, it finds no significant high frequency (low gamma) band power difference in propofol whereas it does in desflurane. In addition, when comparing the HHT results within two groups during unconsciousness, high beta band power in propofol is significantly larger than that of desflurane while delta band power behaves oppositely. In conclusion, this study convincingly shows that EEG analyzed here considerably differs between the HHT and Fourier method.

Design and Evaluation of a Real Time Physiological Signals Acquisition System Implemented in Multi-Operating Rooms for Anesthesia.

With critical importance of medical healthcare, there exist urgent needs for in-depth medical studies that can access and analyze specific physiological signals to provide theoretical support for practical clinical care. As a consequence, obtaining the valuable medical data with minimal cost and impacts on hospital work comes as the first concern of researchers. Anesthesia plays a widely recognized role in surgeries, which attracts people to undertake relevant research. In this paper, a real-time physiological medical signal data acquisition system (PMSDA) for the multi-operating room applications is proposed with high universality of the hospital practical settings and research requirements. By utilizing a wireless communication approach, it provides an easily accessible network platform for collection of physiological medical signals such as photoplethysmogram (PPG), electrocardiograph (ECG) and electroencephalogram (EEG) during the surgery. In addition, the raw data is stored on a server for safe backup and further analysis of depth of anesthesia (DoA). Results show that the PMSDA exhibits robust, high quality performance and efficiently reduces costs compared to previously manual methods and allows seamless integration into hospital environment, independent of its routine work. Overall, it provides a pragmatic and flexible surgery-data acquisition system model with low impact and resource cost applicable to research in critical and practical medical circumstances.

Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries.

Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients' anaesthetic level during surgeries.