Investigation of optical neuro-monitoring technique for detection of maintenance and emergence states during general anesthesia

Functional near-infrared spectroscopy guided mapping of frontal cortex, a novel modality for assessing emergence delirium in children: A prospective observational study

INTRODUCTION

Despite an 18%-30% prevalence, there is no consensus regarding pathogenesis of emergence delirium after anesthesia in children. Functional near-infrared spectroscopy (fNIRS) is an optical neuroimaging modality that relies on blood oxygen level-dependent response, translating to a mean increase in oxyhemoglobin and a decrease in deoxyhemoglobin. We aimed to correlate the emergence delirium in the postoperative period with the changes in the frontal cortex utilizing fNIRS reading primarily and also with blood glucose, serum electrolytes, and preoperative anxiety scores.

METHODS

A total of 145 ASA I and II children aged 2-5 years, undergoing ocular examination under anesthesia, were recruited by recording the modified Yale Preoperative Anxiety Score after acquiring the Institute Ethics Committee approval and written informed parental consent. Induction and maintenance were done with O2, N2O, and Sevoflurane. The emergence delirium was assessed using the PAED score in the postoperative period. The frontal cortex fNIRS recordings were taken throughout anesthesia.

RESULTS

A total of 59 children (40.7%) had emergence delirium. The ED+ group had a significant activation left superior frontal cortex (t = 2.26E+00; p = .02) and right middle frontal cortex (t = 2.27E+00; p = .02) during induction, significant depression in the left middle frontal (t = -2.22E+00; p = .02), left superior frontal and bilateral medial (t = -3.01E+00; p = .003), right superior frontal and bilateral medial (t = -2.44E+00; p = .015), bilateral medial and superior (t = -3.03E+00; p = .003), and right middle frontal cortex (t = -2.90E+00; p = .004) during the combined phase of maintenance, and significant activation in cortical activity in the left superior frontal cortex (t = 2.01E+00; p = .0047) during the emergence in comparison with the ED- group.

CONCLUSION

There is significant difference in the change in oxyhemoglobin concentration during induction, maintenance, and emergence in specific frontal brain regions between children with and without emergence delirium.

Deep Learning Based Walking Tasks Classification in Older Adults Using fNIRS

Decline in gait features is common in older adults and an indicator of increased risk of disability, morbidity, and mortality. Under dual task walking (DTW) conditions, further degradation in the performance of both the gait and the secondary cognitive task were found in older adults which were significantly correlated to falls history. Cortical control of gait, specifically in the pre-frontal cortex (PFC) as measured by functional near infrared spectroscopy (fNIRS), during DTW in older adults has recently been studied. However, the automatic classification of differences in cognitive activations under single and dual task gait conditions has not been extensively studied yet. In this paper, by considering single task walking (STW) as a lower attentional walking state and DTW as a higher attentional walking state, we aimed to formulate this as an automatic detection of low and high attentional walking states and leverage deep learning methods to perform their classification. We conduct analysis on the data samples which reveals the characteristics on the difference between HbO2 and Hb values that are subsequently used as additional features. We perform feature engineering to formulate the fNIRS features as a 3-channel image and apply various image processing techniques for data augmentation to enhance the performance of deep learning models. Experimental results show that pre-trained deep learning models that are fine-tuned using the collected fNIRS dataset together with gender and cognitive status information can achieve around 81% classification accuracy which is about 10% higher than the traditional machine learning algorithms. We present additional sensitivity metrics such as confusion matrix, precision and F1 score, as well as accuracy on two-way classification between condition pairings. We further performed an extensive ablation study to evaluate factors such as the voxel locations, channels of input images, zero-paddings and pre-training of deep learning model on their contribution or impact to the classification task. Results showed that using pre-trained model, all the voxel locations, and HbO2 - Hb as the third channel of the input image can achieve the best classification accuracy.

Diagnosis, management, and prevention of malfunctions in anesthesia machines

BACKGROUND

The anesthesia machine serves as a vital piece of lifesaving equipment.

OBJECTIVE

To analyze incidents of failures in the Primus anesthesia machine and address these malfunctions to reduce recurrence of failure, save maintenance costs, enhance safety, and improve overall efficiency.

METHODS

We conducted an analysis on the records pertaining to the maintenance and parts replacement of the Primus anesthesia machines used in the Department of Anaesthesiology at Shanghai Chest Hospital over the past two years to identify the most common causes of failure. This included an assessment of the damaged parts and degree of damage, as well as a review of factors that caused the fault.

RESULTS

The main cause of the faults in the anesthesia machine was found to be air leakage and excessive humidity in the central air supply of the medical crane. The logistics department was instructed to increase inspections to check and ensure the quality of the central gas supply and ensure gas safety.

CONCLUSION

Summarizing the methods for dealing with anesthesia machine faults can save hospitals a lot of money, ensure normal hospital and department maintenance, and provide a reference to repair such faults. The use of Internet of Things platform technology can continuously develop the direction of digitalization, automation, and intelligent management in each stage of the "whole life cycle" of anesthesia machine equipment.

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.

Monitoring anesthesia using simultaneous functional Near Infrared Spectroscopy and Electroencephalography

OBJECTIVE

This study aims to understand the neural and hemodynamic responses during general anesthesia in order to develop a comprehensive multimodal anesthesia depth monitor using simultaneous functional Near Infrared Spectroscopy (fNIRS) and Electroencephalogram (EEG).

METHODS

37 adults and 17 children were monitored with simultaneous fNIRS and EEG, during the complete general anesthesia process. The coupling of fNIRS signals with neuronal signals (EEG) was calculated. Measures of complexity (sample entropy) and phase difference were also quantified from fNIRS signals to identify unique fNIRS based biomarkers of general anesthesia.

RESULTS

A significant decrease in the complexity and power of fNIRS signals characterize the anesthesia maintenance phase. Furthermore, responses to anesthesia vary between adults and children in terms of neurovascular coupling and frontal EEG alpha power.

CONCLUSIONS

This study shows that fNIRS signals could reliably quantify the underlying neuronal activity under general anesthesia and clearly distinguish the different phases throughout the procedure in adults and children (with less accuracy).

SIGNIFICANCE

A multimodal approach incorporating the specific differences between age groups, provides a reliable measure of anesthesia depth.

A Narrative Review on Clinical Applications of fNIRS

Functional near-infrared spectroscopy (fNIRS) is a relatively new imaging modality in the functional neuroimaging research arena. The fNIRS modality non-invasively investigates the change of blood oxygenation level in the human brain utilizing the transillumination technique. In the last two decades, the interest in this modality is gradually evolving for its real-time monitoring, relatively low-cost, radiation-less environment, portability, patient-friendliness, etc. Including brain-computer interface and functional neuroimaging research, this technique has some important application of clinical perspectives such as Alzheimer's disease, schizophrenia, dyslexia, Parkinson's disease, childhood disorders, post-neurosurgery dysfunction, attention, functional connectivity, and many more can be diagnosed as well as in some form of assistive modality in clinical approaches. Regarding the issue, this review article presents the current scopes of fNIRS in medical assistance, clinical decision making, and future perspectives. This article also covers a short history of fNIRS, fundamental theories, and significant outcomes reported by a number of scholarly articles. Since this review article is hopefully the first one that comprehensively explores the potential scopes of the fNIRS in a clinical perspective, we hope it will be helpful for the researchers, physicians, practitioners, current students of the functional neuroimaging field, and the related personnel for their further studies and applications.

Topics and trends in artificial intelligence assisted human brain research

Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AI-assisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AI-assisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AI-assisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.

Optics Based Label-Free Techniques and Applications in Brain Monitoring

Functional near-infrared spectroscopy (fNIRS) has been utilized already around three decades for monitoring the brain, in particular, oxygenation changes in the cerebral cortex. In addition, other optical techniques are currently developed for in vivo imaging and in the near future can be potentially used more in human brain research. This paper reviews the most common label-free optical technologies exploited in brain monitoring and their current and potential clinical applications. Label-free tissue monitoring techniques do not require the addition of dyes or molecular contrast agents. The following optical techniques are considered: fNIRS, diffuse correlations spectroscopy (DCS), photoacoustic imaging (PAI) and optical coherence tomography (OCT). Furthermore, wearable optical brain monitoring with the most common applications is discussed.

Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization

We conducted a systematic review of literature to better understand the role of new technologies in the perioperative period; in particular we focus on the administrative and managerial Operating Room (OR) perspective. Studies conducted on adult (≥ 18 years) patients between 2015 and February 2019 were deemed eligible. A total of 19 papers were included. Our review suggests that the use of Machine Learning (ML) in the field of OR organization has many potentials. Predictions of the surgical case duration were obtain with a good performance; their use could therefore allow a more precise scheduling, limiting waste of resources. ML is able to support even more complex models, which can coordinate multiple spaces simultaneously, as in the case of the post-anesthesia care unit and operating rooms. Types of Artificial Intelligence could also be used to limit another organizational problem, which has important economic repercussions: cancellation. Random Forest has proven effective in identifing surgeries with high risks of cancellation, allowing to plan preventive measures to reduce the cancellation rate accordingly. In conclusion, although data in literature are still limited, we believe that ML has great potential in the field of OR organization; however, further studies are needed to assess the effective role of these new technologies in the perioperative medicine.

Monitoring the Depth of Anesthesia Through the Use of Cerebral Hemodynamic Measurements Based on Sample Entropy Algorithm

OBJECTIVE

The aim of this study is to explore the relationship between the depth of anesthesia and the cerebral hemodynamic variables during the complete anesthesia process.

METHODS

In this study, near-infrared spectroscopy signals were used to record eight kinds of cerebral hemodynamic variables, including left, right, proximal, distal deoxygenated (Hb) and oxygenated (HbO₂) hemoglobin concentration changes. Then, by measuring the complexity information of cerebral hemodynamic variables, the sample entropy was calculated as a new index of monitoring the depth of anesthesia.

RESULTS

By means of receiver operating characteristic curve analysis, the sample entropy approach was proved to effectively discriminate anesthesia maintenance and waking phases. The discriminatory ability of HbO₂ signals was stronger than that of Hb signals and the distal signals had weaker discrimination capability when compared with the proximal signals. In addition, there was statistical consistency between the bispectral index and sample entropy of cerebral hemodynamic variables during the complete anesthesia process. Moreover, the cerebral hemodynamic signals could not be interfered by clinical electrical devices.

CONCLUSION

The sample entropy of cerebral hemodynamic variables could be suitable as a new index for monitoring the depth of anesthesia.

SIGNIFICANCE

This study is very meaningful for developing new modality and decoding methods in perspective of anesthesia surveillance and may result in the anesthesia monitoring system with high performance.

EOY summary 2018

Clinical monitoring and technology are at the heart of anesthesiology, and new technological developments will help to define how anesthesiology will evolve as a profession. Anesthesia related research published in the JCMC in 2018 mainly pertained to ICU sedation with inhaled agents, anesthesia workstation technology, and monitoring of different aspects of depth of anesthesia.

Investigation of data-driven optical neuromonitoring approach during general anesthesia with sevoflurane

Anesthesia monitoring currently needs a reliable method to evaluate the effects of the anesthetics on its primary target, the brain. This study focuses on investigating the clinical usability of a functional near-infrared spectroscopy (fNIRS)-derived machine learning classifier to perform automated and real-time classification of maintenance and emergence states during sevoflurane anesthesia. For 19 surgical procedures, we examine the entire continuum of the maintenance-transition-emergence phases and evaluate the predictive capability of a support vector machine (SVM) classifier during these phases. We demonstrate the robustness of the predictions made by the SVM classifier and compare its performance with that of minimum alveolar concentration (MAC) and bispectral (BIS) index-based predictions. The fNIRS-SVM investigated in this study provides evidence to the usability of the fNIRS signal for anesthesia monitoring. The method presented enables classification of the signal as maintenance or emergence automatically as well as in real-time with high accuracy, sensitivity, and specificity. The features local mean HbTotal, std [Formula: see text], local min Hb and [Formula: see text], and range Hb and [Formula: see text] were found to be robust biomarkers of this binary classification task. Furthermore, fNIRS-SVM was capable of identifying emergence before movement in a larger number of patients than BIS and MAC.