搜索结果：Anesthesia and analgesia

Objectives. Accurately predicting transitions to anesthetic drugs overdosage is a critical challenge in general anesthesia as it requires the identification of EEG indicators relevant for anticipating the evolution of the depth of anesthesia. Methods. In this study, we introduce a real-time, data-driven framework based on alpha spindle dynamics extracted from frontal EEG recordings. Using Empirical Mode Decomposition, we segment transient alpha spindle events and extract statistical features such as amplitude, duration, frequency, and suppression intervals. We apply these features to train a Light Gradient Boosting Machine, LGBM, classifier on a clinical EEG dataset spanning induction, maintenance, and emergence phases of general anesthesia. Results. Our model accurately classifies anesthesia phases with over 80 percent accuracy and anticipates the onset of isoelectric suppression, a marker of anesthetic drugs overdosage, with 96 percent accuracy up to 90 seconds in advance. Conclusion. The spindle-based metrics provides a non-invasive, interpretable, and predictive approach. This real-time method can be used to forecast unintentional anesthetic drugs overdosage, enabling proactive

Pain management in intensive care usually involves complex trade-offs, since both inadequate and excessive treatment can compromise patient safety. Prior work on reinforcement learning for sedation and analgesia has explored how to optimize these interventions, but has not considered patient survival or partial observability. To investigate the risks of these design choices, we developed an offline deep reinforcement learning framework that suggests hourly medication doses based on recurrent state representations. Using retrospective data from 47,144 ICU stays in the MIMIC-IV database, we trained and evaluated behavior-regularized actor-critic models that prescribe continuous doses of opioids, propofol, benzodiazepines, and dexmedetomidine according to two goals: reduce pain or jointly reduce pain and 30-day post-discharge mortality. Although the two resulting policies were associated with lower pain, clinician agreement with the pain-only policy was positively correlated with mortality ($ρ$=0.119, p<0.0001), while agreement with the joint policy was negatively correlated ($ρ$=-0.316, p<0.0001). We found that such divergence arose from a different response to high levels of c

A general mechanism for anesthetic function is not fully understood. Similarly, the mechanism by which xenon, a chemically inert noble gas, can produce anesthetic effects remains ambiguous. However, a previous study reported a surprisingly strong nuclear-spin-dependent variation in anesthetic potency in mice, although no rigorous molecular mechanism was proposed. This perspective examines that observation and explores a potential connection to the chiral-induced spin selectivity (CISS) effect, a phenomenon that can account for spin-dependent processes in chiral systems. Here we propose a mechanism that links spin-dependent charge organization with chiral molecular systems through a kinetic model that reproduces the reported nuclear spin dependence of xenon anesthesia. The model is based on the nuclear spin-dependent permeability of isotopes through homochiral media, which modulates biological function through ligand-receptor binding in analogy with the Hill-Langmuir equation. Unlike mechanisms that require long-range quantum coherence, our framework remains robust under physiological, room-temperature conditions because it relies on the intrinsic stability of the CISS effect in dis

Accurately predicting anesthetic effects is essential for target-controlled infusion systems. The traditional (PK-PD) models for Bispectral index (BIS) prediction require manual selection of model parameters, which can be challenging in clinical settings. Recently proposed deep learning methods can only capture general trends and may not predict abrupt changes in BIS. To address these issues, we propose a transformer-based method for predicting the depth of anesthesia (DOA) using drug infusions of propofol and remifentanil. Our method employs long short-term memory (LSTM) and gate residual network (GRN) networks to improve the efficiency of feature fusion and applies an attention mechanism to discover the interactions between the drugs. We also use label distribution smoothing and reweighting losses to address data imbalance. Experimental results show that our proposed method outperforms traditional PK-PD models and previous deep learning methods, effectively predicting anesthetic depth under sudden and deep anesthesia conditions.

The U.S. Food and Drug Administration has cautioned that prenatal exposure to anesthetic drugs during the third trimester may have neurotoxic effects; however, there is limited clinical evidence available to substantiate this recommendation. One major scientific question of interest is whether such neurotoxic effects might be due to surgery, anesthesia, or both. Isolating the effects of these two exposures is challenging because they are observationally equivalent, thereby inducing an extreme positivity violation. To address this, we adopt the separable effects framework of Robins and Richardson (2010) to identify the effect of anesthesia (alone) by blocking effects through variables that are assumed to completely mediate the causal pathway from surgery to the outcome. We apply this approach to data from the nationwide Medicaid Analytic eXtract (MAX) from 1999 through 2013, which linked 16,778,281 deliveries to mothers enrolled in Medicaid during pregnancy. Furthermore, we assess the sensitivity of our results to violations of our key identification assumptions.

The growing availability of high-resolution, long-term time series data has highlighted the need for methods capable of capturing both local and global patterns. To address this, we introduce the Probabilistic Visibility Graph (PVG), a novel approach inspired by the quantum tunnelling phenomenon. The PVG extends the classical Visibility Graph (VG) by introducing probabilistic connections between time points that are obstructed in the VG due to intermediate values. We demonstrate the PVG's effectiveness in capturing long-range dependencies through simulations of amplitude-modulated signals and analysis of electrocorticography (ECoG) data under rest and anesthesia conditions. Key results show that the PVG presents distinct network properties between rest and anesthesia, with rest exhibiting stronger small-worldness and scale-free behavior, reflecting a hub-dominated, centralized connectivity structure, compared to anesthesia. These findings highlight the PVG's potential for analyzing complex signals with interacting temporal scales, offering new insights into neural dynamics and other real-world phenomena.

Visual servoing for the development of autonomous robotic systems capable of administering UltraSound (US) guided regional anesthesia requires real-time segmentation of nerves, needle tip localization and needle trajectory extrapolation. First, we recruited 227 patients to build a large dataset of 41,000 anesthesiologist annotated images from US videos of brachial plexus nerves and developed models to localize nerves in the US images. Generalizability of the best suited model was tested on the datasets constructed from separate US scanners. Using these nerve segmentation predictions, we define automated anesthesia needle targets by fitting an ellipse to the nerve contours. Next, we developed an image analysis tool to guide the needle toward their targets. For the segmentation of the needle, a natural RGB pre-trained neural network was first fine-tuned on a large US dataset for domain transfer and then adapted for the needle using a small dataset. The segmented needle trajectory angle is calculated using Radon transformation and the trajectory is extrapolated from the needle tip. The intersection of the extrapolated trajectory with the needle target guides the needle navigation for

Conscious state estimation is important in various medical settings, including sleep staging and anesthesia management, to ensure patient safety and optimize health outcomes. Traditional methods predominantly utilize electroencephalography (EEG), which faces challenges such as high sensitivity to noise and the requirement for controlled environments. In this study, we propose the consciousness-ECG transformer that leverages electrocardiography (ECG) signals for non-invasive and reliable conscious state estimation. Our approach employs a transformer with decoupled query attention to effectively capture heart rate variability features that distinguish between conscious and unconscious states. We implemented the conscious state estimation system with real-time monitoring and validated our system on datasets involving sleep staging and anesthesia level monitoring during surgeries. Experimental results demonstrate that our model outperforms baseline models, achieving accuracies of 0.877 on sleep staging and 0.880 on anesthesia level monitoring. Moreover, our model achieves the highest area under curve values of 0.786 and 0.895 on sleep staging and anesthesia level monitoring, respective

The dynamical mechanism underlying the processes of anesthesia-induced loss of consciousness and recovery is key to gaining insights into the working of the nervous system. Previous experiments revealed an asymmetry between neural signals during the anesthesia and recovery processes. Here we obtain experimental evidence for the hysteresis loop and articulate the dynamical mechanism based on percolation on multilayer complex networks with self-similarity. Model analysis reveals that, during anesthesia, the network is able to maintain its neural pathways despite the loss of a substantial fraction of the edges. A predictive and potentially testable result is that, in the forward process of anesthesia, the average shortest path and the clustering coefficient of the neural network are markedly smaller than those associated with the recovery process. This suggests that the network strives to maintain certain neurological functions by adapting to a relatively more compact structure in response to anesthesia.

Post-operative cognitive decline is a well-known phenomenon and of crucial importance especially in the elderly. General anesthesia can be accomplished by inhalation-based (volatile) or total intravenous anesthesia (TIVA). While their effects on post-operative symptoms have been investigated, little is known about their influence on brain functionalities during the surgery itself. To assess differences 17 patients were divided to receive either volatile anesthesia (n=9), or TIVA (n=8). The level of anesthesia was kept to be equal in both groups. A single bipolar EEG electrode (Neurosteer system) was placed on the participants foreheads. It presented real-time activity and collected their data during the surgery. The dependent variables included frequency bands (delta, theta, alpha, and beta), and three features (VC9, ST4, and A0) previously extracted with the device and provided by Neurosteer. All surgeries were uneventful, and all patients showed bispectral index (BIS) score less than 60. Feature activity under volatile anesthesia (in comparison to TIVA) was significantly lower for the delta, theta and alpha frequency bands and for the three features. Further analysis showed that

Parametric differential equations of the form du/dt = f(u, x, t, p) are fundamental in science and engineering. While deep learning frameworks such as the Fourier Neural Operator (FNO) can efficiently approximate solutions, they struggle with inverse problems, sensitivity estimation (du/dp), and concept drift. We address these limitations by introducing a sensitivity-based regularization strategy, called Sensitivity-Constrained Fourier Neural Operators (SC-FNO). SC-FNO achieves high accuracy in predicting solution paths and consistently outperforms standard FNO and FNO with physics-informed regularization. It improves performance in parameter inversion tasks, scales to high-dimensional parameter spaces (tested with up to 82 parameters), and reduces both data and training requirements. These gains are achieved with a modest increase in training time (30% to 130% per epoch) and generalize across various types of differential equations and neural operators. Code and selected experiments are available at: https://github.com/AMBehroozi/SC_Neural_Operators

The case experience of anesthesiologists is one of the leading causes of accidental dural punctures and failed epidurals - the most common complications of epidural analgesia used for pain relief during delivery. We designed a bimanual haptic simulator to train anesthesiologists and optimize epidural analgesia skill acquisition. We present an assessment study conducted with 22 anesthesiologists of different competency levels from several Israeli hospitals. Our simulator emulates the forces applied to the epidural (Touhy) needle, held by one hand, and those applied to the Loss of Resistance (LOR) syringe, held by the other one. The resistance is calculated based on a model of the epidural region layers parameterized by the weight of the patient. We measured the movements of both haptic devices and quantified the results' rate (success, failed epidurals, and dural punctures), insertion strategies, and the participants' answers to questionnaires about their perception of the simulation realism. We demonstrated good construct validity by showing that the simulator can distinguish between real-life novices and experts. Face and content validity were examined by studying users' impressio

Variations of instantaneous heart rate appears regularly oscillatory in deeper levels of anesthesia and less regular in lighter levels of anesthesia. It is impossible to observe this "rhythmic-to-non-rhythmic" phenomenon from raw electrocardiography waveform in current standard anesthesia monitors. To explore the possible clinical value, I proposed the adaptive harmonic model, which fits the descriptive property in physiology, and provides adequate mathematical conditions for the quantification. Based on the adaptive harmonic model, multitaper Synchrosqueezing transform was used to provide time-varying power spectrum, which facilitates to compute the quantitative index: "Non-rhythmic-to-Rhythmic Ratio" index (NRR index). I then used a clinical database to analyze the behavior of NRR index and compare it with other standard indices of anesthetic depth. The positive statistical results suggest that NRR index provides addition clinical information regarding motor reaction, which aligns with current standard tools. Furthermore, the ability to indicates the noxious stimulation is an additional finding. Lastly, I have proposed an real-time interpolation scheme to contribute my study furt

We apply techniques from the field of computational mechanics to evaluate the statistical complexity of neural recording data from fruit flies. First, we connect statistical complexity to the flies' level of conscious arousal, which is manipulated by general anesthesia (isoflurane). We show that the complexity of even single channel time series data decreases under anesthesia. The observed difference in complexity between the two states of conscious arousal increases as higher orders of temporal correlations are taken into account. We then go on to show that, in addition to reducing complexity, anesthesia also modulates the informational structure between the forward- and reverse-time neural signals. Specifically, using three distinct notions of temporal asymmetry we show that anesthesia reduces temporal asymmetry on information-theoretic and information-geometric grounds. In contrast to prior work, our results show that: (1) Complexity differences can emerge at very short timescales and across broad regions of the fly brain, thus heralding the macroscopic state of anesthesia in a previously unforeseen manner, and (2) that general anesthesia also modulates the temporal asymmetry of

Anesthetic agents are known to induce a range of alterations in cortical electrophysiological activity, such as the rise of signature patterns, changes in statistical properties, and altered dynamic behavior of neural records. Plenty of methods can be used to monitor these changes, among them complexity metrics demonstrated to have the power to discriminate states involving distinct levels of awareness. There is a consensus that anesthetic drugs can interfere with neural activities at different levels and time scales, being able to induce alterations both locally and in the spatiotemporal patterns established throughout the whole cortex. However, it is still unclear how such changes in the complexity of cortical activity are supposed to occur, and experimental evidence is still needed. For this purpose, we have analyzed an ECoG records database of a Ketamine-Medetomidine anesthetic induction in a non-human primate subject. The MDR-ECoG technique provided records of cortical activity with both high temporal and spatial resolution allied with extensive coverage of the cortical surface. The Permutation Entropy and the Fractal Dimension were employed to evaluate the complexity of the n

One of the most important surgical factors is Depth of Anesthesia (DOA) control in patients. The main problem is to overcome the uncertainty and nonlinearity of the system, due to different physiological parameters of the patient's body and maintain DOA of patients in desired range during surgery. This study demonstrates a fractional order fuzzy PID controller (FOFPID) and fractional order PID controller (FOPID) to the problem. The Whale Optimization Algorithms (WOA) is used to optimized the parameters of proposed controllers. The orders of derivative and integral fractional controller is achieved by WOA. The results indicate that FOFPID has a better performance than FOPID. To check the performance of the controllers in presence of uncertainty, physiological logical model of 8 patients has been investigated. The modeling is based on Pharmacodynamic and Pharmacokinetic model. The results show the performance of the proposed method.

Some anesthetics bind and potentiate gamma-aminobutyric-acid-type receptors, but no universal mechanism for general anesthesia is known. Furthermore, often encountered complications such as anesthesia induced amnesia are not understood. General anesthetics are hydrophobic molecules easily dissolving into lipid bilayers. Recently, it was shown that general anesthetics perturb phase separation in vesicles extracted from fixed cells. Unclear is whether under physiological conditions general anesthetics induce perturbation of the lipid bilayer, and whether this contributes to the transient loss of consciousness or anesthesia side effects. Here we show that propofol perturbs lipid nanodomains in the outer and inner leaflet of the plasma membrane in intact cells, affecting membrane nanodomains in a concentration dependent manner: 1 μM to 5 μM propofol destabilize nanodomains; however, propofol concentrations higher than 5 μM stabilize nanodomains with time. Stabilization occurs only at physiological temperature and in intact cells. This process requires ARP2/3 mediated actin nucleation and Myosin II activity. The rate of nanodomain stabilization is potentiated by GABA receptor activity.

In this paper, an MPC for tracking formulation is proposed for the control of anesthesia dynamics. It seamlessly enables the optimization of the steady-states pair that is not unique due to the MISO nature of the model. Anesthesia dynamics is a multi-time scale system with two types of states characterized, respectively, by fast and slow dynamics. In anesthesia control, the output equation depends only on the fast dynamics. Therefore, the slow states can be treated as disturbances, and compensation terms can be introduced. Subsequently, the system can be reformulated as a nominal one allowing the design of an MPC for tracking strategy. The presented framework ensures recursive feasibility and asymptotic stability, through the design of appropriate terminal ingredients in the MPC for tracking framework. The controller performance is then assessed on a patient in a simulation environment.

Anesthetics are crucial in surgical procedures and therapeutic interventions, but they come with side effects and varying levels of effectiveness, calling for novel anesthetic agents that offer more precise and controllable effects. Targeting Gamma-aminobutyric acid (GABA) receptors, the primary inhibitory receptors in the central nervous system, could enhance their inhibitory action, potentially reducing side effects while improving the potency of anesthetics. In this study, we introduce a proteomic learning of GABA receptor-mediated anesthesia based on 24 GABA receptor subtypes by considering over 4000 proteins in protein-protein interaction (PPI) networks and over 1.5 millions known binding compounds. We develop a corresponding drug-target interaction network to identify potential lead compounds for novel anesthetic design. To ensure robust proteomic learning predictions, we curated a dataset comprising 136 targets from a pool of 980 targets within the PPI networks. We employed three machine learning algorithms, integrating advanced natural language processing (NLP) models such as pretrained transformer and autoencoder embeddings. Through a comprehensive screening process, we ev