Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia

SQI-DOANet: electroencephalogram-based deep neural network for estimating signal quality index and depth of anaesthesia

Objective. Monitoring the depth of anaesthesia (DOA) during surgery is of critical importance. However, during surgery electroencephalography (EEG) is usually subject to various disturbances that affect the accuracy of DOA. Therefore, accurately estimating noise in EEG and reliably assessing DOA remains an important challenge. In this paper, we proposed a signal quality index (SQI) network (SQINet) for assessing the EEG signal quality and a DOA network (DOANet) for analyzing EEG signals to precisely estimate DOA. The two networks are termed SQI-DOANet.Approach. The SQINet contained a shallow convolutional neural network to quickly determine the quality of the EEG signal. The DOANet comprised a feature extraction module for extracting features, a dual attention module for fusing multi-channel and multi-scale information, and a gated multilayer perceptron module for extracting temporal information. The performance of the SQI-DOANet model was validated by training and testing the model on the large VitalDB database, with the bispectral index (BIS) as the reference standard.Main results. The proposed DOANet yielded a Pearson correlation coefficient with the BIS score of 0.88 in the five-fold cross-validation, with a mean absolute error (MAE) of 4.81. The mean Pearson correlation coefficient of SQI-DOANet with the BIS score in the five-fold cross-validation was 0.82, with an MAE of 5.66.Significance. The SQI-DOANet model outperformed three compared methods. The proposed SQI-DOANet may be used as a new deep learning method for DOA estimation. The code of the SQI-DOANet will be made available publicly athttps://github.com/YuRui8879/SQI-DOANet.

Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors

Precision medicine, characterized by the personalized integration of a patient's genetic blueprint and clinical history, represents a dynamic paradigm in healthcare evolution. The emerging field of personalized anesthesia is at the intersection of genetics and anesthesiology, where anesthetic care will be tailored to an individual's genetic make-up, comorbidities and patient-specific factors. Genomics and biomarkers can provide more accurate anesthetic protocols, while artificial intelligence can simplify anesthetic procedures and reduce anesthetic risks, and real-time monitoring tools can improve perioperative safety and efficacy. The aim of this paper is to present and summarize the applications of these related fields in anesthesiology by reviewing them, exploring the potential of advanced technologies in the implementation and development of personalized anesthesia, realizing the future integration of new technologies into clinical practice, and promoting multidisciplinary collaboration between anesthesiology and disciplines such as genomics and artificial intelligence.

Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment

Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures. Traditional methods of depth of anaesthesia (DoA) assessment, reliant on physical characteristics, have proven inconsistent due to individual variations. In response, electroencephalography (EEG) techniques have emerged, with indices such as the Bispectral Index offering quantifiable assessments. This literature review explores the current scope and frontier of DoA research, emphasising methods utilising EEG signals for effective clinical monitoring. This review offers a critical synthesis of recent advances, specifically focusing on electroencephalography (EEG) techniques and their role in enhancing clinical monitoring. By examining 117 high-impact papers, the review delves into the nuances of feature extraction, model building, and algorithm design in EEG-based DoA analysis. Comparative assessments of these studies highlight their methodological approaches and performance, including clinical correlations with established indices like the Bispectral Index. The review identifies knowledge gaps, particularly the need for improved collaboration for data access, which is essential for developing superior machine learning models and real-time predictive algorithms for patient management. It also calls for refined model evaluation processes to ensure robustness across diverse patient demographics and anaesthetic agents. The review underscores the potential of technological advancements to enhance precision, safety, and patient outcomes in anaesthesia, paving the way for a new standard in anaesthetic care. The findings of this review contribute to the ongoing discourse on the application of EEG in anaesthesia, providing insights into the potential for technological advancement in this critical area of medical practice.

Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology

Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in turn challenged clinicians, hospital administrators, and health policymakers to understand such technologies and develop frameworks for safe and sustained clinical implementation. Within cardiac anesthesiology, challenges and opportunities for AI/ML to support patient care are presented by the vast amounts of electronic health data, which are collected rapidly, interpreted, and acted upon within the periprocedural area. To address such challenges and opportunities, in this article, the authors review 3 recent applications relevant to cardiac anesthesiology, including depth of anesthesia monitoring, operating room resource optimization, and transthoracic/transesophageal echocardiography, as conceptual examples to explore strengths and limitations of AI/ML within healthcare, and characterize this evolving landscape. Through reviewing such applications, the authors introduce basic AI/ML concepts and methodologies, as well as practical considerations and ethical concerns for initiating and maintaining safe clinical implementation of AI/ML-based algorithms for cardiac anesthesia patient care.

Artificial intelligence and its clinical application in Anesthesiology: a systematic review

PURPOSE

Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context.

METHODS

A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted.

RESULTS

A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods.

CONCLUSION

AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.

A Retrospective Study of the Patient State Index During General Anesthesia in Infants and Young Children

This study described electroencephalogram (EEG) parameters in children under general anesthesia, which could monitor patient-specific brain responses to anesthetics and assess the effects of anesthesia. The objective was to detect the patient state index (PSI) and associated factors. We analyzed EEG parameters in patients in the age range 1 to 36 months. Patients were stratified into 2 groups as those aged 1 to 12 months and 13 to 36 months. Sixty-two patients were involved. Spectral edge frequency (SEF), PSI, and blood pressure were lower, and burst suppression rate (BSR) and heart rate were higher in the 1 to 12 months group. The SEF was associated with PSI in both groups. Age and blood pressure were positively associated with PSI, and BSR was negatively related to PSI in children under 1 year of age. Blood pressure was not associated with PSI in the 13 to 36 months age group. We found that the PSI levels did not accurately assess the depth of anesthesia in children under 1 year of age.

How anesthesiology can deal with innovation and new technologies?

Innovation and new technologies have always impacted significantly the anesthesiology practice all along the perioperative course, as it is recognized as one of the most transformative medical specialties specifically regarding patient's safety. Beside a number of major changes in procedures, equipment, training, and organization that aggregated to establish a strong safety culture with effective practices, anesthesiology is also a stakeholder in disruptive innovation. The present review is not exhaustive and aims to provide an overview on how innovation could change and improve anesthesiology practices through some examples as telemedicine (TM), machine learning and artificial intelligence (AI). For example, postoperative complications can be accurately predicted by AI from automated real-time electronic health record data, matching physicians' predictive accuracy. Clinical workflow could be facilitated and accelerated with mobile devices and applications, assuming that these tools should remain at the service of patients and care providers. Care providers and patients connections have improved, thanks to these digital and innovative transformations, without replacing existing relationships between them. It also should give time back to physicians and nurses to better spend it in the perioperative care, and to provide "personalized" medicine keeping a high level of standard of care.

Classification of First-Episode Psychosis with EEG Signals: ciSSA and Machine Learning Approach

First-episode psychosis (FEP) typically marks the onset of severe psychiatric disorders and represents a critical period in the field of mental health. The early diagnosis of this condition is essential for timely intervention and improved clinical outcomes. In this study, the classification of FEP was investigated using the analysis of electroencephalography (EEG) signals and circulant spectrum analysis (ciSSA) sub-band signals. FEP poses a significant diagnostic challenge in the realm of mental health, and it is aimed at introducing a novel and effective approach for early diagnosis. To achieve this, the LASSO method was utilized to select the most significant features derived from entropy, frequency, and statistical-based characteristics obtained from ciSSA sub-band signals, as well as their hybrid combinations. Subsequently, a high-performance classification model has been developed using machine learning techniques, including ensemble, support vector machine (SVM), and artificial neural network (ANN) methods. The results of this study demonstrated that the hybrid features extracted from EEG signals' ciSSA sub-bands, in combination with the SVM method, achieved a high level of performance, with an area under curve (AUC) of 0.9893, an accuracy of 96.23%, a sensitivity of 0.966, a specificity of 0.956, a precision of 0.9667, and an F1 score of 0.9666. This has revealed the effectiveness of the ciSSA-based method for classifying FEP from EEG signals.

Development of a Bispectral index score prediction model based on an interpretable deep learning algorithm

BACKGROUND

Proper maintenance of hypnosis is crucial for ensuring the safety of patients undergoing surgery. Accordingly, indicators, such as the Bispectral index (BIS), have been developed to monitor hypnotic levels. However, the black-box nature of the algorithm coupled with the hardware makes it challenging to understand the underlying mechanisms of the algorithms and integrate them with other monitoring systems, thereby limiting their use.

OBJECTIVE

We propose an interpretable deep learning model that forecasts BIS values 25 s in advance using 30 s electroencephalogram (EEG) data.

MATERIAL AND METHODS

The proposed model utilized EEG data as a predictor, which is then decomposed into amplitude and phase components using fast Fourier Transform. An attention mechanism was applied to interpret the importance of these components in predicting BIS. The predictability of the model was evaluated on both regression and binary classification tasks, where the former involved predicting a continuous BIS value, and the latter involved classifying a dichotomous status at a BIS value of 60. To evaluate the interpretability of the model, we analyzed the attention values expressed in the amplitude and phase components according to five ranges of BIS values. The proposed model was trained and evaluated using datasets collected from two separate medical institutions.

RESULTS AND CONCLUSION

The proposed model achieved excellent performance on both the internal and external validation datasets. The model achieved a root-mean-square error of 6.614 for the regression task, and an area under the receiver operating characteristic curve of 0.937 for the binary classification task. Interpretability analysis provided insight into the relationship between EEG frequency components and BIS values. Specifically, the attention mechanism revealed that higher BIS values were associated with increased amplitude attention values in high-frequency bands and increased phase attention values in various frequency bands. This finding is expected to facilitate a more profound understanding of the BIS prediction mechanism, thereby contributing to the advancement of anesthesia technologies.

Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security

The rapid development of artificial intelligence (AI) technology is due to the significant progress in big data, databases, algorithms, and computing power, and medical research is a vital application direction of AI. The integrated development of AI and medicine has improved medical technology, and the efficiency of medical services and equipment has enabled doctors to better serve patients. The tasks and characteristics of the anesthesia discipline also make AI necessary for its development, and AI has also been initially applied in different fields of anesthesia. Our review aims to clarify the current situation and challenges of AI application in anesthesiology to provide clinical references and guide the future development of AI in anesthesiology. This review summarizes progress in the application of AI in perioperative risk assessment and prediction, deep monitoring and regulation of anesthesia, essential anesthesia skills operation, automatic drug administration systems, and teaching and training in anesthesia. Also discussed herein are the accompanying risks and challenges of applying AI in anesthesia: patient privacy and information security, data sources, and ethical issues, lack of capital and talent, and the "black box" phenomenon.

Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network

The reliable monitoring of the depth of anesthesia (DoA) is essential to control the anesthesia procedure. Electroencephalography (EEG) has been widely used to estimate DoA since EEG could reflect the effect of anesthetic drugs on the central nervous system (CNS). In this study, we propose that a deep learning model consisting mainly of a deep residual shrinkage network (DRSN) and a 1 × 1 convolution network could estimate DoA in terms of patient state index (PSI) values. First, we preprocessed the four raw channels of EEG signals to remove electrical noise and other physiological signals. The proposed model then takes the preprocessed EEG signals as inputs to predict PSI values. Then we extracted 14 features from the preprocessed EEG signals and implemented three conventional feature-based models as comparisons. A dataset of 18 patients was used to evaluate the models' performances. The results of the five-fold cross-validation show that there is a relatively high similarity between the ground-truth PSI values and the predicted PSI values of our proposed model, which outperforms the conventional models, and further, that the Spearman's rank correlation coefficient is 0.9344. In addition, an ablation experiment was conducted to demonstrate the effectiveness of the soft-thresholding module for EEG-signal processing, and a cross-subject validation was implemented to illustrate the robustness of the proposed method. In summary, the procedure is not merely feasible for estimating DoA by mimicking PSI values but also inspired us to develop a precise DoA-estimation system with more convincing assessments of anesthetization levels.

Open Reimplementation of the BIS Algorithms for Depth of Anesthesia

BACKGROUND

BIS (a brand of processed electroencephalogram [EEG] depth-of-anesthesia monitor) scores have become interwoven into clinical anesthesia care and research. Yet, the algorithms used by such monitors remain proprietary. We do not actually know what we are measuring. If we knew, we could better understand the clinical prognostic significance of deviations in the score and make greater research advances in closed-loop control or avoiding postoperative cognitive dysfunction or juvenile neurological injury. In previous work, an A-2000 BIS monitor was forensically disassembled and its algorithms (the BIS Engine) retrieved as machine code. Development of an emulator allowed BIS scores to be calculated from arbitrary EEG data for the first time. We now address the fundamental questions of how these algorithms function and what they represent physiologically.

METHODS

EEG data were obtained during induction, maintenance, and emergence from 12 patients receiving customary anesthetic management for orthopedic, general, vascular, and neurosurgical procedures. These data were used to trigger the closely monitored execution of the various parts of the BIS Engine, allowing it to be reimplemented in a high-level language as an algorithm entitled ibis. Ibis was then rewritten for concision and physiological clarity to produce a novel completely clear-box depth-of-anesthesia algorithm titled openibis .

RESULTS

The output of the ibis algorithm is functionally indistinguishable from the native BIS A-2000, with r = 0.9970 (0.9970-0.9971) and Bland-Altman mean difference between methods of -0.25 ± 2.6 on a unitless 0 to 100 depth-of-anesthesia scale. This precision exceeds the performance of any earlier attempt to reimplement the function of the BIS algorithms. The openibis algorithm also matches the output of the native algorithm very closely ( r = 0.9395 [0.9390-0.9400], Bland-Altman 2.62 ± 12.0) in only 64 lines of readable code whose function can be unambiguously related to observable features in the EEG signal. The operation of the openibis algorithm is described in an intuitive, graphical form.

CONCLUSIONS

The openibis algorithm finally provides definitive answers about the BIS: the reliance of the most important signal components on the low-gamma waveband and how these components are weighted against each other. Reverse engineering allows these conclusions to be reached with a clarity and precision that cannot be obtained by other means. These results contradict previous review articles that were believed to be authoritative: the BIS score does not appear to depend on a bispectral index at all. These results put clinical anesthesia research using depth-of-anesthesia scores on a firm footing by elucidating their physiological basis and enabling comparison to other animal models for mechanistic research.

Real-Time Depth of Anaesthesia Assessment Based on Hybrid Statistical Features of EEG

This paper proposed a new depth of anaesthesia (DoA) index for the real-time assessment of DoA using electroencephalography (EEG). In the proposed new DoA index, a wavelet transform threshold was applied to denoise raw EEG signals, and five features were extracted to construct classification models. Then, the Gaussian process regression model was employed for real-time assessment of anaesthesia states. The proposed real-time DoA index was implemented using a sliding window technique and validated using clinical EEG data recorded with the most popular commercial DoA product Bispectral Index monitor (BIS). The results are evaluated using the correlation coefficients and Bland-Altman methods. The outcomes show that the highest and the average correlation coefficients are 0.840 and 0.814, respectively, in the testing dataset. Meanwhile, the scatter plot of Bland-Altman shows that the agreement between BIS and the proposed index is 94.91%. In contrast, the proposed index is free from the electromyography (EMG) effect and surpasses the BIS performance when the signal quality indicator (SQI) is lower than 15, as the proposed index can display high correlation and reliable assessment results compared with clinic observations.

Developing a robust model to predict depth of anesthesia from single channel EEG signal

Monitoring depth of anaesthesia (DoA) from electroencephalograph (EEG) signals is an ongoing challenge for anaesthesiologists. In this study, we propose an intelligence model that predicts the DoA from a single channel electroencephalograph (EEG) signal. A segmentation technique based on a sliding window is employed to partition EEG signals. Hierarchical dispersion entropy (HDE) is applied to each EEG segment. A set of features is extracted from each EEG segment. The extracted features are investigated using a community graph detection approach (CGDA), and the most relevant features are selected to trace the DoA. The proposed model, based on HDE coupled with CGDA, is evaluated in term of BIS index using several statistical metrics such Q-Q plot, regression, and correlation coefficients. In addition, the proposed model is evaluated against the BIS index in the case of the poor signal quality. The results demonstrated that the proposed model showed an earlier reaction compared with the BIS index when patient’s state transits from deep anaesthesia to moderate anaesthesia in the case of poor signal quality. The highest Pearson correlation coefficient obtained by the proposed is 0.96.

A novel empirical wavelet SODP and spectral entropy based index for assessing the depth of anaesthesia

The requirement for anaesthesia during modern surgical procedures is unquestionable to ensure a safe experience for patients with successful recovery. Assessment of the depth of anaesthesia (DoA) is an important and ongoing field of research to ensure patient stability during and post-surgery. This research addresses the limitations of current DoA indexes by developing a new index based on electroencephalography (EEG) signal analysis. Empirical wavelet transformation (EWT) methods are employed to extract wavelet coefficients before statistical analysis. The features Spectral Entropy and Second Order Difference Plot are extracted from the wavelet coefficients. These features are used to train a new index, SSE_DoA, utilising a Support Vector Machine (SVM) with a linear kernel function. The new index accurately assesses the DoA to illustrate the transition between different anaesthetic stages. Testing was undertaken with nine patients and an additional four patients with low signal quality. Across the nine patients we tested, an average correlation of 0.834 was observed with the Bispectral (BIS) index. The analysis of the DoA stage transition exhibited a Choen's Kappa of 0.809, indicative of a high agreement.

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Propofol Anesthesia Depth Monitoring Based on Self-Attention and Residual Structure Convolutional Neural Network

Methods

We compare nine index values, select CNN+EEG, which has good correlation with BIS index, as an anesthesia state observation index to identify the parameters of the model, and establish a model based on self-attention and dual resistructure convolutional neural network. The data of 93 groups of patients were selected and randomly grouped into three parts: training set, validation set, and test set, and compared the best and worst results predicted by BIS.

Result

The best result is that the model's accuracy of predicting BLS on the test set has an overall upward trend, eventually reaching more than 90%. The overall error shows a gradual decrease and eventually approaches zero. The worst result is that the model's accuracy of predicting BIS on the test set has an overall upward trend. The accuracy rate is relatively stable without major fluctuations, but the final accuracy rate is above 70%.

Conclusion

The prediction of BIS indicators by the deep learning method CNN algorithm shows good results in statistics.

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.

The Investigation of Behavior Change in EEG Signals During Induction of Anesthesia

Anesthesiology aims to make anesthesia safer and increase the precision of prognoses. Correct assessment of the anesthesia depth is crucial to its safety. At present, intraoperative electroencephalogram (EEG) monitoring is the primary mode of anesthesia depth monitoring and judgment. However, most clinical anesthesiologists rely on commercial anesthesia depth monitors to judge anesthesia depth, such as bispectral index (BIS) and patient state index (PSI). This may lack an understanding of associated changes in brain wave quantization. Therefore, this study conducts quantitative analyses of EEG signals during anesthesia induction. EEG signals are processed within specific time windows and extracted brainpower density spectrum arrays with different frequency bands, brain electrical signal spectra, source frequencies and other key indicators. Analysis and comparison of these indicators clarifies patterns of variation in EEG signals during early anesthesia induction. The spectral edge frequencies (SEFs) of EEG signals within different time windows can be modeled accurately, from which the specific time points of EEG signal changes are derived. Furthermore, the relationship between patient age and the effect of anesthetic drugs is preliminarily investigated by analyzing the SEF variations of different age groups. This study quantifies changes in the EEG signals of patients at the initial stage of anesthesia induction and drug-related effects are observed, which opens a way for further exploration of EEG changes in patients under general anesthesia.

A Review on the Current Applications of Artificial Intelligence in the Operating Room

Background. Artificial intelligence (AI) is an era upcoming in medicine and, more recently, in the operating room (OR). Existing literature elaborates mainly on the future possibilities and expectations for AI in surgery. The aim of this study is to systematically provide an overview of the current actual AI applications used to support processes inside the OR. Methods. PubMed, Embase, Cochrane Library, and IEEE Xplore were searched using inclusion criteria for relevant articles up to August 25th, 2020. No study types were excluded beforehand. Articles describing current AI applications for surgical purposes inside the OR were reviewed. Results. Nine studies were included. An overview of the researched and described applications of AI in the OR is provided, including procedure duration prediction, gesture recognition, intraoperative cancer detection, intraoperative video analysis, workflow recognition, an endoscopic guidance system, knot-tying, and automatic registration and tracking of the bone in orthopedic surgery. These technologies are compared to their, often non-AI, baseline alternatives. Conclusions. Currently described applications of AI in the OR are limited to date. They may, however, have a promising future in improving surgical precision, reduce manpower, support intraoperative decision-making, and increase surgical safety. Nonetheless, the application and implementation of AI inside the OR still has several challenges to overcome. Clear regulatory, organizational, and clinical conditions are imperative for AI to redeem its promise. Future research on use of AI in the OR should therefore focus on clinical validation of AI applications, the legal and ethical considerations, and on evaluation of implementation trajectory.

Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a Multi-Factor Clinical Test

Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention. We used simple clinical tests, including the timed-up and go test (TUG), short form berg balance scale (SFBBS), and short portable mental status questionnaire (SPMSQ) to measure different factors related to postural stability that have been found to increase the risk of falling. We attached an inertial sensor to the lower back of a group of elderly subjects while they performed the TUG test, providing us with a tri-axial acceleration signal, which we used to extract a set of features, including multi-scale entropy (MSE), permutation entropy (PE), and statistical features. Using the score for each clinical test, we classified our participants into fallers or non-fallers in order to (1) compare the features calculated from the inertial sensor data, and (2) compare the screening capabilities of the multifactor clinical test against each individual test. We use random forest to select features and classify subjects across all scenarios. The results show that the combination of MSE and statistic features overall provide the best classification results. Meanwhile, PE is not an important feature in any scenario in our study. In addition, a t-test shows that the multifactor test of TUG and BBS is a better classifier of subjects in this study.

Consciousness and complexity: a consilience of evidence

Over the last years, a surge of empirical studies converged on complexity-related measures as reliable markers of consciousness across many different conditions, such as sleep, anesthesia, hallucinatory states, coma, and related disorders. Most of these measures were independently proposed by researchers endorsing disparate frameworks and employing different methods and techniques. Since this body of evidence has not been systematically reviewed and coherently organized so far, this positive trend has remained somewhat below the radar. The aim of this paper is to make this consilience of evidence in the science of consciousness explicit. We start with a systematic assessment of the growing literature on complexity-related measures and identify their common denominator, tracing it back to core theoretical principles and predictions put forward more than 20 years ago. In doing this, we highlight a consistent trajectory spanning two decades of consciousness research and provide a provisional taxonomy of the present literature. Finally, we consider all of the above as a positive ground to approach new questions and devise future experiments that may help consolidate and further develop a promising field where empirical research on consciousness appears to have, so far, naturally converged.

Comprehensive evaluation of the combined extracts of Epimedii Folium and Ligustri Lucidi Fructus for PMOP in ovariectomized rats based on MLP-ANN methods

ETHNOPHARMACOLOGICAL RELEVANCE

Kidney deficiency is the main pathogenesis of osteoporosis based on the theory of "kidney governing bones" in traditional Chinese medicine (TCM). Osteoporosis is a systemic disease; kidney deficiency influences the growth, aging and reproduction of human body, reflecting in endocrine, nerve, immunity, metabolism and other functions. Multi-target drugs composed of natural non-toxic products from kidney-reinforcing herbs, are being investigated for the treatment of osteoporosis. Therefore, it is necessary and imperative to develop an objective and comprehensive method to evaluate and compare the effects of herbs with listed drugs.

AIM OF THE STUDY

This study was designed to evaluate and compare the therapeutic effects and the underlying molecular mechanism of the combined extracts of Epimedii Folium and Ligustri Lucidi Fructus (EL) with Raloxifene hydrochloride (RH) in ovariectomy (OVX)-induced postmenopausal osteoporosis (PMOP) rats based on the multi-layer perception (MLP)-artificial neural network (ANN) model.

MATERIALS AND METHODS

Female SD rats were subjected to either sham surgery (n = 8) or bilateral OVX (n = 48). One week after recovering from surgery, the OVX-induced rats were randomly divided into three groups: OVX model group (n = 32, every 8 rats were killed at the end of the 5th, 9th, 11th or 13th week after OVX), EL group (treated with EL 0.35 g/kg, n = 8), and RH group (treated with RH 6.25 mg/kg, n = 8). The rats in the treatment groups were administrated once a day for 12 weeks, then sacrificed. We observed bone mass and quality, bone remodeling, the function of estrogen and TGF-β1/Smads pathway in all rats.

RESULTS

Both EL and RH could increase bone mineral density, enhance bone strength, relieve bone micro-structure degeneration, re-balance bone remodeling, regulate estrogen dysfunction, and up-regulate TGF-β1 expression. The evaluation of the MLP-ANN model showed that EL and RH had markedly anti-PMOP effects, and there was no significant difference in the comprehensive evaluation of anti-osteoporosis between the two drugs. However, RH had better effects on bone mass and quality and TGF-β1/Smads pathway than EL; EL had better effects on estrogen function than RH.

CONCLUSION

Combined extracts of Epimedii Folium and Ligustri Lucidi Fructus (EL) exhibited bone-protective effects on PMOP. The MLP-ANN method evaluated the efficacy of drugs more comprehensively, which provided a new direction for the evaluation and comparison of drugs.

Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states

Background

Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, we hypothesize that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states, providing a secondary tool for DoA assessment.

Methods

A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived features in the time and frequency domain combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which utilized the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method.

Results

The accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods.

Conclusions

The incorporation of four HRV-derived features in the time and frequency domain and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot feasibility study. The proposed method—with other evaluation methods, such as EEG—is expected to assist anaesthesiologists in the accurate evaluation of the DoA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01285-x.

A Forensic Disassembly of the BIS Monitor

BACKGROUND

The bispectral index (BIS) monitor has been available for clinical use for >20 years and has had an immense impact on academic activity in Anesthesiology, with >3000 articles referencing the bispectral index. Despite attempts to infer its algorithms by external observation, its operation has nevertheless remained undescribed, in contrast to the algorithms of other less commercially successful monitors of electroencephalogram (EEG) activity under anesthesia. With the expiration of certain key patents, the time is therefore ripe to examine the operation of the monitor on its own terms through careful dismantling, followed by extraction and examination of its internal software.

METHODS

An A-2000 BIS Monitor (gunmetal blue case, amber monochrome display) was purchased on the secondary market. After identifying the major data processing and storage components, a set of free or inexpensive tools was used to retrieve and disassemble the monitor's onboard software. The software executes primarily on an ARMv7 microprocessor (Sharp/NXP LH77790B) and a digital signal processor (Texas Instruments TMS320C32). The device software can be retrieved directly from the monitor's hardware by using debugging interfaces that have remained in place from its original development.

RESULTS

Critical numerical parameters such as the spectral edge frequency (SEF), total power, and BIS values were retraced from external delivery at the device's serial port back to the point of their calculation in the extracted software. In doing so, the locations of the critical algorithms were determined. To demonstrate the validity of the technique, the algorithms for SEF and total power were disassembled, comprehensively annotated and compared to their theoretically ideal behaviors. A bug was identified in the device's implementation of the SEF algorithm, which can be provoked by a perfectly isoelectric EEG.

CONCLUSIONS

This article demonstrates that the electronic design of the A-2000 BIS Monitor does not pose any insuperable obstacles to retrieving its device software in hexadecimal machine code form directly from the motherboard. This software can be reverse engineered through disassembly and decompilation to reveal the methods by which the BIS monitor implements its algorithms, which ultimately must form the definitive statement of its function. Without further revealing any algorithms that might be considered trade secrets, the manufacturer of the BIS monitor should be encouraged to release the device software in its original format to place BIS-related academic literature on a firm theoretical foundation and to promote further academic development of EEG monitoring algorithms.

Estimating Postural Stability Using Improved Permutation Entropy via TUG Accelerometer Data for Community-Dwelling Elderly People

To develop an effective fall prevention program, clinicians must first identify the elderly people at risk of falling and then take the most appropriate interventions to reduce or eliminate preventable falls. Employing feature selection to establish effective decision making can thus assist in the identification of a patient's fall risk from limited data. This work therefore aims to supplement professional timed up and go assessment methods using sensor technology, entropy analysis, and statistical analysis. The results showed the different approach of applying logistic regression analysis to the inertial data on a fall-risk scale to allow medical practitioners to predict for high-risk patients. Logistic regression was also used to automatically select feature values and clinical judgment methods to explore the differences in decision making. We also calculate the area under the receiver-operating characteristic curve (AUC). Results indicated that permutation entropy and statistical features provided the best AUC values (all above 0.9), and false positives were avoided. Additionally, the weighted-permutation entropy/statistical features test has a relatively good agreement rate with the short-form Berg balance scale when classifying patients as being at risk. Therefore, the proposed methodology can provide decision-makers with a more accurate way to classify fall risk in elderly people.

Prediction value of preoperative findings on meningioma grading using artificial neural network

OBJECTIVES

Meningioma is the most common brain tumor in adults. Grade 1 meningiomas have excellent prognoses, but grades 2 and 3 usually have worse outcomes, higher recurrence rates, and higher mortality rates. Preoperative determination of tumor grade may be helpful in deciding the type of surgery and the rate of resection. Blood markers have been used to predict the rate of malignancy and prognosis of tumors in different regions, including the brain. The current study investigated the use of blood markers on predicting meningioma grade.

PATIENTS AND METHODS

Patients with newly diagnosed meningiomas were retrospectively reviewed. Data on the patients' demographics, tumor locations, blood markers, and tumor pathology grades was extracted. The relationship between preoperative findings and tumor grade was statistically analyzed, and using the same findings and an artificial neural network, the accuracy of tumor grade prediction was evaluated.

RESULTS

This study included 95 patients, 69 cases (72.4 %) of grade 1, 23 cases of grade 2 (24.4 %) and 3 cases of grade 3 (3.2 %) meningiomas. Monocyte and neutrophil counts as well as lymphocyte-to-monocyte ratio (LMR) were significantly different between low grade and high grade meningiomas, with higher monocyte and neutrophil counts and higher LMR associated with high grade meningiomas (p < 0.05). Evaluation of the data with an artificial neural network using RBF with 5 variables (age, monocyte count, LMR, platelet-to-lymphocyte ratio (PLR), and neutrophil count) indicated that tumor grade can be determined with 83 % accuracy using an artificial neural network.

CONCLUSION

A preoperative high monocyte count and high LMR are associated with high grade meningioma. An artificial neural network using preoperative data can acceptably be used to characterize meningioma tumor grades.

Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals

Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject's influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject's influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.