Depth of anaesthesia assessment based on time and frequency features of simplified electroencephalogram (EEG)

Anaesthesiology is a medical subject focusing on the use of drugs and other methods to deprive patients’ sensation for discomfort in painful medical diagnosis or treatment. It is important to assess the depth of anaesthesia (DoA) accurately since a precise as- sessment is helpful for avoiding various adverse reactions such as intraoperative awareness with recall (underdosage), prolonged recovery and an increased risk of post- operative complications for a patient (overdosage). Evidence shows that the depth of anaesthesia monitoring using electroencephalograph (EEG) improves patient treat- ment outcomes by reducing the incidences of intra-operative awareness, minimizing anaesthetic drug consumption and resulting in faster wake-up and recovery. For an accurate DoA assessment, intensive research has been conducted in finding 'an ulti- mate index', and various monitors and DoA algorithms were developed. Generally, the limitations of the existing DoA monitors or latest DoA algorithms include unsatis- factory data filtering techniques, time delay and inflexible. The focus of this dissertation is to develop reliable DoA algorithms for accurate DoA assessment. Some novel time-frequency domain signal processing techniques, which are better suited for non-stationary EEG signals than currently established methods, have been proposed and applied to monitor the DoA based on simplified EEG signals based on plenty of programming work (including C and other programming language). The fast Fourier transform (FFT) and the discrete wavelet transforms are applied to pre-process EEG data in the frequency domain. The nonlocal mean, mobility, permu- tation entropy, Lempel-Ziv complexity, second order difference plot and interval feature extraction methods are modified and applied to investigate the scaling behaviour of the EEG in the time domain. We proposed and developed three new indexes for identifying, classifying and monitoring the DoA. The new indexes are evaluated by comparing with the most popular BIS index. Simulation results demonstrate that our new methods monitor the DoA in all anaesthesia states accurately. The results also demonstrate the advantages of proposed indexes in the cases of poor signal quality and the consistency with the anaesthetists’ records. These new indexes show a 3.1-59.7 seconds earlier time response than BIS during the change from awake to light anaesthesia and a 33-264 seconds earlier time response than BIS during the change from deep anaesthesia to moderate anaesthesia

Robust approach for depth of anaesthesia assessment based on hybrid transform and statistical features

To develop an accurate and efficient depth of anaesthesia (DoA) assessment technique that could help anaesthesiologists to trace the patient’s anaesthetic state during surgery, a new automated DoA approach was proposed. It applied Wavelet-Fourier analysis (WFA) to extract the statistical characteristics from an anaesthetic EEG signal and to designed a new DoA index. In this proposed method, firstly, the wavelet transform was applied to a denoised EEG signal, and a Fast Fourier transform was then applied to the wavelet detail coefficient D3. Ten statistical features were extracted and analysed, and from these, five features were selected for designing a new index for the DoA assessment. Finally, a new DoA (WFADoA) was developed and compared with the most popular bispectral index (BIS) monitor. The results from the testing set showed that there were very high correlations between the WFADoA and the BIS index during the awake, light and deep anaesthetic stages. In the case of poor signal quality, the BIS index and the WFADoA were also tested, and the obtained results demonstrated that the WFADoA could indicate the DoA values, while the BIS failed to show valid outputs for those situations

EEG sleep stages identification based on weighted undirected complex networks

Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks. Methods each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks. Results In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals. Conclusions An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard

Real time depth of anaesthesia monitoring through electroencephalogram (EEG) signal analysis based on Bayesian method and analytical technique

The electroencephalogram (EEG) signal from the brain is used for analysing brain abnormality, diseases, and monitoring patient conditions during surgery. One of the applications of the EEG signals analysis is real-time anaesthesia monitoring, as the anaesthetic drugs normally targeted the central nervous system. Depth of anaesthesia has been clinically assessed through breathing pattern, heart rate, arterial blood pressure, pupil dilation, sweating and the presence of movement. Those assessments are useful but are an indirect-measurement of anaesthetic drug effects. A direct method of assessment is through EEG signals because most anaesthetic drugs affect neuronal activity and cause a changed pattern in EEG signals. The aim of this research is to improve real-time anaesthesia assessment through EEG signal analysis which includes the filtering process, EEG features extraction and signal analysis for depth of anaesthesia assessment. The first phase of the research is EEG signal acquisition. When EEG signal is recorded, noises are also recorded along with the brain waves. Therefore, the filtering is necessary for EEG signal analysis. The filtering method introduced in this dissertation is Bayesian adaptive least mean square (LMS) filter which applies the Bayesian based method to find the best filter weight step for filter adaptation. The results show that the filtering technique is able to remove the unwanted signals from the EEG signals. This dissertation proposed three methods for EEG signal features extraction and analysing. The first is the strong analytical signal analysis which is based on the Hilbert transform for EEG signal features' extraction and analysis. The second is to extract EEG signal features using the Bayesian spike accumulation technique. The third is to apply the robust Bayesian Student-t distribution for real-time anaesthesia assessment. Computational results from the three methods are analysed and compared with the recorded BIS index which is the most popular and widely accepted depth of anaesthesia monitor. The outcomes show that computation times from the three methods are leading the BIS index approximately 18-120 seconds. Furthermore, the responses to anaesthetic drugs are verified with the anaesthetist's documentation and then compared with the BIS index to evaluate the performance. The results indicate that the three methods are able to extract EEG signal features efficiently, improve computation time, and respond faster to anaesthetic drugs compared to the existing BIS index

Quasi-Periodicities Detection Using Phase-Rectified Signal Averaging in EEG Signals as a Depth of Anesthesia Monitor

Phase-rectified signal averaging (PRSA) has been known to be a useful method to detect periodicities in non-stationary biological signals. Determination of quasi-periodicities in electroencephalogram (EEG) is a candidate for quantifying the changes of depth of anesthesia (DOA). In this paper, DOA monitoring capacity of periodicities detected using PRSA were quantified by assessing EEG signals collected from 56 patients during surgery. The method is compared to sample entropy (SampEn), detrended fluctuation analysis (DFA) and permutation entropy (PE). The performance of quasi-periodicities defined by acceleration capacity (AC) and deceleration capacity (DC) was tested using the area under the receiver operating characteristic curve (AUC) and Pearson correlation coefficient. During the surgery, a significant difference (p < 0.05) in the quasi-periodicities was observed among three different stages under general anesthesia. There is a larger mean AUC and correlation coefficient of quasi-periodicities compared to SampEn, DFA and PE using expert assessment of conscious level (EACL) and bispectral index (BIS) as the gold standard, respectively. Quasi-periodicities detected using PRSA in EEG signals are powerful monitor of DOA and perform more accurate and robust results compared to SampEn, DFA and PE. The results do provide a valuable reference to researchers in the filed of clinical applications.10.13039/501100003711-Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by the Ministry of Science and Technology; 10.13039/501100001809-National Natural Science Foundation of China

Analysis of consciousness for complete locked-in syndrome patients

This thesis presents methods for detecting consciousness in patients with complete locked-in syndrome (CLIS). CLIS patients are unable to speak and have lost all muscle movement. Externally, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to be still conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is vital to develop alternative ways to re-establish communication with these patients during periods of awareness, and a possible platform is through brain–computer interface (BCI). Since consciousness is required to use BCI correctly, this study proposes a modus operandi to analyze not only in intracranial electrocorticography (ECoG) signals with greater signal-to-noise ratio (SNR) and higher signal amplitude, but also in non-invasive electroencephalography (EEG) signals. By applying three different time-domain analysis approaches sample entropy, permutation entropy, and Poincaré plot as feature extraction to prevent disease-related reductions of brainwave frequency bands in CLIS patients, and cross-validated to improve the probability of correctly detecting the conscious states of CLIS patients. Due to the lack a of 'ground truth' that could be used as teaching input to correct the outcomes, k-Means and DBSCAN these unsupervised learning methods were used to reveal the presence of different levels of consciousness for individual participation in the experiment first in locked-in state (LIS) patients with ALSFRS-R score of 0. The results of these different methods converge on the specific periods of consciousness of CLIS/LIS patients, coinciding with the period during which CLIS/LIS patients recorded communication with an experimenter. To determine methodological feasibility, the methods were also applied to patients with disorders of consciousness (DOC). The results indicate that the use of sample entropy might be helpful to detect awareness not only in CLIS/LIS patients but also in minimally conscious state (MCS)/unresponsive wakefulness syndrome (UWS) patients, and showed good resolution for both ECoG signals up to 24 hours a day and EEG signals focused on one or two hours at the time of the experiment. This thesis focus on consistent results across multiple channels to avoid compensatory effects of brain injury. Unlike most techniques designed to help clinicians diagnose and understand patients' long-term disease progression or distinguish between different disease types on the clinical scales of consciousness. The aim of this investigation is to develop a reliable brain-computer interface-based communication aid eventually to provide family members with a method for short-term communication with CLIS patients in daily life, and at the same time, this will keep patients' brains active to increase patients' willingness to live and improve their quality of life (QOL)

The Ecology, Distribution and Spawning Behaviour of the Commercially Important Common Cuttlefish (Sepia officinalis) in the Inshore Waters of the English Channel

Reduced file size version uploaded on 31.10.19 by LW (LED)Over the last 50 years there has been a rapid increase in global landings of cephalopods (octopus, squid and cuttlefish). In European waters, cuttlefish are among the most important commercial cephalopod resources and within the North-East Atlantic, the English Channel supports the largest cuttlefish fishery, with the common cuttlefish, Sepia o cinalis (Linnaeus, 1758), dominating landings. S. o cinalis has a short (2 year) life cycle in the English Channel that is punctuated by seasonal migrations inshore and o shore. Using a combination of di erent métiers including beam trawling, otter trawling and coastal trapping, this shared fisheries resource is targeted at nearly every phase of the life cycle. Despite this continuing increase there remain only minimal management measures in place, with no quotas, no total allowable catches, no closed areas, no minimal landing size and no routine assessment of stocks. In order to provide sustainable fisheries management advice for S. o cinalis populations it is essential that a thorough understanding of the ecology and life history of this species, in particular the factors a ecting spawning and recruitment variability, is attained.In this thesis, I examine critical gaps in our understanding of the distribution, movements, habitat use and behaviours of spawning and sub-adult S. o cinalis. This research provides baseline data for this species within the inshore waters of the English Channel and uses a combination of novel field-based electronic tracking techniques, in situ subtidal observations of spawning patterns within natural environments and presence-only species distribution modelling. A maximum entropy (MaxEnt) modelling approach was used to predict the distribution of benthic egg clusters using presence-only data. The model showed very good performance in terms of predictive power and accuracy (test area under the receiver operating characteristics curve [AUC] = 0.909) and among the explanatory variables used to build the model, depth (gain = 1.17), chlorophyll-a concentration (used here as a proxy for turbidity; gain = 1.06) and distance from coastline (gain = 1.02) were shown to be the greatest determining factors for the distribution of S. o cinalis spawning. As part of the model output, maps (logistic and binary) of the predicted spawning distribution of S. o cinalis within the English Channel were produced.Subtidal observation were undertaken at spawning grounds on both the North and South coast of the English Channel to investigate spawning habitat and structure use. A total of 15 types of natural spawning structures were identified. The range of spawning structures used varied among sites with Zostera marina identified as the dominant spawning structure at two of the UK sites (Torbay and Poole Bay), potentially indicating a ‘preference’ for this structure within localities. Fractal dimension analysis of the seagrass beds at Torbay revealed that the spatial dynamics of seagrass beds within this site varied significantly between 2011 and 2012 (Mann- Whitney U: Z = 4.92, P < 0.0001) as a result of both anthropogenic and natural disturbance. Interannual changes in the spatial dynamics of these beds could a ect the annual pattern and intensity of spawning at a site. The use of structures with small diameters was found to occur, with cuttlefish adapting the device to their requirements by utilising multiple leaves or thalli in order to achieve a suitable diameter for egg attachment, this was evident in their use of both Chorda filum and Z. marina.This research also provided the first data on the fine-scale movements and behaviours of adult and sub-adult individuals, tracked within their natural environments, using electronic tagging methodologies. That expected patterns of short-term spawning site fidelity at a local level were observed in only two individuals, whilst larger scale movements (up to 35 km) along the coastline were observed in three individuals, indicated that a range of behaviours and movement patterns could occur among spawning adults. Similarly varied patterns of site fidelity were also observed in tagged sub-adults, tracked over an extended period (up to 73 days), using a static acoustic array. These results highlight the complex range of patterns and plasticity in behaviour that exist within natural populations.In summary, a series of di erent approaches was used within this thesis in an e ort to improve our understanding of the fine-scale movement, behaviours and habitat use of S. o cinalis (in both spawning adults and non spawning sub-adults), as well as their potential spawning distribution within the inshore waters of the English Channel. Observing the movements and behaviours of small marine animals like S. o cinalis in their natural environments has traditionally been di cult. Recent developments in technologies and techniques however, including those used within this thesis (e.g. electronic tagging), have highlighted the potential capacity of novel tools to monitor the in situ movements and behaviour of cuttlefish. By providing important insights into the ecology of this species these new tools can aid conservation and management advice for this important commercial fishery species, both within the English Channel and further afield.Marine Biological Association of the United Kingdom; EU Interreg IV funded

Study and prediction of time of recovery of consciousness after general anaesthesia

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Gambús Cerrillo, Pedro LuisSeveral studies address the process of loss of consciousness during the induction of general anaesthesia, but few of them discuss or study the process of recovery of consciousness once the of general anaesthesia has been administered successfully. The main objective of this project is to study and develop a predictive model of the duration of this process of consciousness recovery based on Machine Learning (ML) and the analysis of electroencephalographic (EEG) signals. A dataset comprising 143 patients from the 4th operating room of the Hospital Clínic of Barcelona was analysed. The project involved data pre-processing, including the segmentation of EEG signals during the recovery process, feature extraction, and correlation analysis. Five ML regression algorithms, namely Linear, Lasso, and Ridge Regression, Support Vector Regression (SVR), and Random Forest (RF), were evaluated using a Cross-Validation pipeline. Model performance, feature selection, and hyperparameter optimization were assessed using the R-squared score criterion. The best performing algorithm was the regularized linear regression model, Lasso, achieving an R-squared score of 0.74 ± 0.032 (mean and standard deviation). Through the correlation analysis and the feature selection performed by the algorithm, high predictive capabilities of consciousness recovery time were obtained for alpha and beta relative averaged band power in the first minute before stopping general anaesthesia administration. The findings demonstrate that EEG signals contain valuable information regarding the process of consciousness recovery, enabling the construction of ML predictive models. However, further studies are required to enhance our understanding of the consciousness recovery process and to validate the predictive model in a clinical setting. Future investigations should focus on increasing data variability, addressing biases in validation techniques, exploring additional EEG channels to capture global brain activity, and considering regulatory considerations for Artificial Intelligence algorithms

Exploring the pharmacodynamics of multidrug combinations and using the advances in technology to individualise anaesthetic drug titration

In current practice, pharmacokinetic-dynamic (PK/PD) models are frequently used to describe the combined relationship between the time course of drug plasma concentrations (PK) and the time independent relationship between the drug concentration at the receptor site and the clinical effect (PD). This thesis contributes to the knowledge in anaesthetic pharmacology and explores the dose-response relationships of propofol and sevoflurane (with and without the coadministration of remifentanil) in greater detail using PK/PD models. Our studies show that PK/PD models are useful in clinical practice. The concept of neural inertia could have an influence on these models, but is still controversial in humans and it does not break down the essence and applicability of these PK/PD models. Subsequently, we used these models to compare the pharmacodynamics of propofol and sevoflurane (with and without remifentanil) at both a population level as well as at an individual level. This comparison let us describe potency ratios between both hypnotics which is very helpful for anaesthetist when switching between these drugs for any reason during a case. We applied the same PK/PD models and similar potency ratios in clinical practice using the SmartPilot® View, a drug advisory system, to guide anaesthetic drug titration, and we assessed its clinical utility. Finally, we evaluated a novel method to analyse the cerebral drug effect on the EEG using Artificial Intelligence in order to explore the feasibility of whether a single index can quantify the hypnotic effect in a drug-independent way

Functional connectivity analysis in health and brain disease using in vivo widefield calcium imaging

Stroke is one of the leading causes of death and its prevalence is still raising with aging society in future. Despite its major impact, only two specific therapies are approved in clinical practice, today.Thus, hundreds of possible therapies were identified in experimental research, none of them has proven efficiency on human patients. To address this loss in translation from experimental research to clinical practise, several fronts can be scrutinized. Among several options, the establishment of translational methods to assess functional clinical outcome in preclinical research is inevitable. To approach this one option is to develop modalities of functional imaging of the brain activity. Functional brain imaging not only allows to assess translational parameters for functional regeneration after stroke but also to investigate pathophysiological mechanisms in the brain. Hence, the analysis of functional brain activity in experimental stroke research could both identify new therapeutic targets and validate their effectiveness by creating a translational read-out. Functional brain imaging is a frequently used method which strongly advanced our knowledge in neuroscience and as well in human stroke research. Its aim in general is a better understanding of brain functions, identification of functionally connected brain regions and their dynamic changes under certain conditions. In stroke research, the dynamic changes of functional network and its association with regeneration is of major interest. To investigate functional brain activity, functional magnetic resonance imaging (fMRI) is predominantly used in human research. fMRI faces great technical challenges and essential limitations for use in small rodents such as laboratory mice which are the most frequently used animals to study brain disease. This is why there is interest and need for alternative imaging modalities in experimental research. To benefit of the insights from human research in experimental research, we adapted and evolved the imaging modality of in vivo widefield calcium imaging. This imaging modality is based on transgenic animals who permit to investigate brain activity directly via GCaMP fluorescence. GCaMP is a genetically encoded calcium sensor which is well-known to mirror calcium fluctuations during action potential and with this neuronal activity. Via a customized imaging system, it is possible to acquire cortical neuronal activity and analyse it with comparable methods as used in human brain research. Hence, this method allows the repetitive investigation of brain activity in vivo in a translational manner. In three studies we adapted and enhanced existing protocols to establish a reliable transgenic approach to assess functional brain connectivity. In a first study, we investigated the effect of anaesthesia on brain function and characterized the relationship of different frequency-based imaging parameters, functional connectivity and depth of anaesthesia. Subsequently, we established a stringent protocol for light sedation which is easy to use and results in reproducible imaging parameters. In a second study, we identified functional brain areas by using independent vector analysis (IVA) on resting state imaging data. Therefore, we validated the identified areas with help of an anatomical atlas and stimulus-evoked brain activity. This validation justifies the usage of our unbiasedly selected cortical areas as functional seeds. Finally, we implemented the assessment of functional connectivity values after stroke. In this third study, we investigated repetitively the changes in functional connectivity up to 56 days after an ischemic lesion in the motor cortex induced by a photothrombotic model. We demonstrate both acute and chronic effects of ischemia to cortical functional connectivity. In the acute phase on the first day after stroke we demonstrate transient increase in contralateral functional connectivity. A second transient effect is the increase in contralateral motor cortex size. Third, chronic reduction in interhemispheric functional connectivity is present only in functionally but not anatomically close regions of the brain. And last, changes in both functional connectivity values and the size of contralateral motor cortex size are associated with the deficits assessed by behavioural testing. Hence, the identified parameters are of major relevance for the clinical outcome. The results establish two major facts: preclinical investigation of brain function is possible on a routinely basis and adds additional insight on pathophysiological mechanisms in brain disease which are associated with behavioural outcome. Consequently, the application of this translational imaging modality will not only be of great interest to stroke research but also to several brain diseases where pathophysiological mechanisms still need to be elucidated