Future of neurocritical care:Integrating neurophysics,multimodal monitoring,and machine learning

Multimodal monitoring(MMM)in the intensive care unit(ICU)has become increasingly sophisticated with the integration of neurophysical principles.However,the challenge remains to select and interpret the most appropriate combination of neuromonitoring modalities to optimize patient outcomes.This manuscript reviewed current neuromonitoring tools,focusing on intracranial pressure,cerebral electrical activity,metabolism,and invasive and noninvasive autoregulation moni-toring.In addition,the integration of advanced machine learning and data science tools within the ICU were discussed.Invasive monitoring includes analysis of intracranial pressure waveforms,jugular venous oximetry,monitoring of brain tissue oxygenation,thermal diffusion flowmetry,electrocorticography,depth electroencephalography,and cerebral microdialysis.Noninvasive measures include transcranial Doppler,tympanic membrane displacement,near-infrared spectroscopy,optic nerve sheath diameter,positron emission tomography,and systemic hemodynamic monitoring including heart rate variability analysis.The neurophysical basis and clinical relevance of each method within the ICU setting were examined.Machine learning algorithms have shown promise by helping to analyze and interpret data in real time from continuous MMM tools,helping clinicians make more accurate and timely decisions.These algorithms can integrate diverse data streams to generate predictive models for patient outcomes and optimize treatment strategies.MMM,grounded in neurophysics,offers a more nuanced understanding of cerebral physiology and disease in the ICU.Although each modality has its strengths and limitations,its integrated use,especially in combination with machine learning algorithms,can offer invaluable information for individualized patient care.

A time-synchronized multimodal monitoring system for general anesthesia

In clinical settings,different kinds of patient monitoring systems and depth of anesthesia monitoring(DoA)systems have been widely used to assess the depth of sedation and patient's state.However,all these monitoring systems are independent of each other.To date,no monitoring system has focused on the synchronized neural activities,cerebral metabolism,autonomic nervous system,and drug effects on the brain,as well as their interactions between neural activities and cerebral metabolism(i.e.,neurovascular coupling),and between brain and heart(i.e.,brain-heart coupling).In this study,we developed a time-synchronized multimodal monitoring system(TSMMS)that integrates electroencephalogram(EEG),near-infrared spectroscopy(NIRS),and standard physiological monitors(electrocardiograph,blood pressure,oxygen saturation)to provide a comprehensive view of the patient's physiological state during surgery.The coherence and Granger causality(GC)methods were used to quantify the neurovascular coupling and brain-heart coupling.The response surface model was used to estimate the combined propofol-remifentanil drug effect on the brain.TSMMS integrates data from various devices for a comprehensive analysis of vital signs.It enhances anesthesia monitoring through detailed EEG features,neurovascular,and brain-heart coupling indicators.Additionally,a response surface model estimates the combined effects of propofol and remifentanil,aiding anesthesiologists in drug administration.In conclusion,TSMMS provides a new tool for studying the coupling mechanism among neural activities,cerebral metabolism,and autonomic nervous system during general anesthesia.

Multimodal process monitoring based on transition-constrained Gaussian mixture model

Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is challenging.Most multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical application.This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring.This technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online data.This process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode overlap.Case studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap.

Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

A novel multimode process monitoring method integrating LCGMM with modified LFDA

Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.

Quality oriented multimode processes monitoring based on a novel hierarchical common and specific structure with different order information

Due to higher demands on product diversity,flexible shift between productions of different products in one equipment becomes a popular solution,resulting in existence of multiple operation modes in a single process.In order to handle such multi-mode process,a novel double-layer structure is proposed and the original data are decomposed into common and specific characteristics according to the relationship between variables among each mode.In addition,both low and high order information are considered in each layer.The common and specific information within each mode can be captured and separated into several subspaces according to the different order information.The performance of the proposed method is further validated through a numerical example and the Tennessee Eastman(TE)benchmark.Compared with previous methods,superiority of the proposed method is validated by the better monitoring results.

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

Anti-fatigue ionic gels for long-term multimodal respiratory abnormality monitoring

Wearable electronics integrated with stretchable sensors are considered a promising and non-invasive strategy to monitor respiratory status for health assessment.However,long-term and stable monitoring of respiratory abnormality is still a grand challenge.Here,we report a facile one-step thermal stretching strategy to fabricate an anti-fatigue ionic gel(AIG)sensor with high fatigue threshold(0=1130 J m^(–2)),high stability(>20,000 cycles),high linear sensitivity,and recyclability.A multimodal wearable respiratory monitoring system(WRMS)developed with AIG sensors can continuously measure respiratory abnormality(single-sensor mode)and compliance(multi-sensor mode)by monitoring the movement of the ribcage and abdomen in a long-term manner.For single-sensor mode,the respiratory frequency(Fr),respiratory energy(Er),and inspire/expire time(I/E ratio)can be extracted to evaluate the respiratory status during sitting,sporting,and sleeping.Further,the multi-sensors mode is developed to evaluate patientventilator asynchrony through validated clinical criteria by monitoring the incongruous movement of the chest and abdomen,which shows great potential for both daily home care and clinical applications.

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

A local discriminant regularized soft k-means （LDRSKM） method with Bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft k-means algorithm by exploiting the local and non-local geometric information of the data and generalized linear discriminant analysis to provide a better and more meaningful data partition. LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of data residing in different clusters. With the data partition obtained, kernel support vector data description （KSVDD） is used to establish the monitoring statistics and control limits. Two Bayesian inference based global fault detection indicators are then developed using the local monitoring results associated with principal and residual subspaces. Based on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode correlations, and local geometric information can be exploited to enhance monitoring performances for nonlinear and non-Gaussian processes. The effectiveness and efficiency of the proposed method are evaluated using the Tennessee Eastman benchmark process.

Effects of positive end-expiratory pressure on intracranial pressure,cerebral perfusion pressure,and brain oxygenation in acute brain injury:Friend or foe?A scoping review

Background Patients with acute brain injury(ABI)are a peculiar population because ABI does not only affect the brain but also other organs such as the lungs,as theorized in brain–lung crosstalk models.ABI patients often require mechanical ventilation(MV)to avoid the complications of impaired respiratory function that can follow ABI;MV should be settled with meticulousness owing to its effects on the intracranial compartment,especially regarding positive end-expiratory pressure(PEEP).This scoping review aimed to(1)describe the physiological basis and mechanisms related to the effects of PEEP in ABI;(2)examine how clinical research is conducted on this topic;(3)identify methods for setting PEEP in ABI;and(4)investigate the impact of the application of PEEP in ABI on the outcome.Methods The five-stage paradigm devised by Peters et al.and expanded by Arksey and O'Malley,Levac et al.,and the Joanna Briggs Institute was used for methodology.We also adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)extension criteria.Inclusion criteria:we compiled all scientific data from peer-reviewed journals and studies that discussed the application of PEEP and its impact on intracranial pressure,cerebral perfusion pressure,and brain oxygenation in adult patients with ABI.Exclusion criteria:studies that only examined a pediatric patient group(those under the age of 18),experiments conducted solely on animals;studies without intracranial pressure and/or cerebral perfusion pressure determinations,and studies with incomplete information.Two authors searched and screened for inclusion in papers published up to July 2023 using the PubMed-indexed online database.Data were presented in narrative and tubular form.Results The initial search yielded 330 references on the application of PEEP in ABI,of which 36 met our inclusion criteria.PEEP has recognized beneficial effects on gas exchange,but it produces hemodynamic changes that should be predicted to avoid undesired consequences on cerebral blood flow and intracranial pressure.Moreover,the elastic properties of the lungs influence the transmission of the forces applied by MV over the brain so they should be taken into consideration.Currently,there are no specific tools that can predict the effect of PEEP on the brain,but there is an established need for a comprehensive monitoring approach for these patients,acknowledging the etiology of ABI and the measurable variables to personalize MV.Conclusion PEEP can be safely used in patients with ABI to improve gas exchange keeping in mind its potentially harmful effects,which can be predicted with adequate monitoring supported by bedside non-invasive neuromonitoring tools.