Anesthesia assessment based on ICA permutation entropy analysis of two-channel EEG signals

Inaccurate assessment may lead to inaccurate levels of dosage given to the patients that may lead to intraoperative awareness that is caused by under dosage during surgery or prolonged recovery in patients that is caused by over dosage after the surgery is done. Previous research and evidence show that assessing anesthetic levels with the help of electroencephalography (EEG) signals gives an overall better aspect of the patient’s anesthetic state. This paper presents a new method to assess the depth of anesthesia (DoA) using Independent Component Analysis (ICA) and permutation entropy analysis. ICA is performed on two-channel EEG to reduce the noise then Wavelet and permutation entropy are applied on these channels to extract the features. A linear regression model was used to build the new DoA index using the selected features. The new index designed by proposed methods performs well under low signal quality and it was overall consistent in most of the cases where Bispectral index (BIS) may fail to provide any valid value

Computational methods for resting-state EEG of patients with disorders of consciousness

Patients who survive brain injuries may develop Disorders of Consciousness (DOC) such as Coma, Vegetative State (VS) or Minimally Conscious State (MCS). Unfortunately, the rate of misdiagnosis between VS and MCS due to clinical judgment is high. Therefore, diagnostic decision support systems aiming to correct any differentiation between VS and MCS are essential for the characterization of an adequate treatment and an effective prognosis. In recent decades, there has been a growing interest in the new EEG computational techniques. We have reviewed how resting-state EEG is computationally analyzed to support differential diagnosis between VS and MCS in view of applicability of these methods in clinical practice. The studies available so far have used different techniques and analyses; it is therefore hard to draw general conclusions. Studies using a discriminant analysis with a combination of various factors and reporting a cut-off are among the most interesting ones for a future clinical application

Functional assessment of bidirectional cortical and peripheral neural control on heartbeat dynamics: A brain-heart study on thermal stress

The study of functional Brain-Heart Interplay (BHI) from non-invasive recordings has gained much interest in recent years. Previous endeavors aimed at understanding how the two dynamical systems exchange information, providing novel holistic biomarkers and important insights on essential cognitive aspects and neural system functioning. However, the interplay between cardiac sympathovagal and cortical oscillations still has much room for further investigation. In this study, we introduce a new computational framework for a functional BHI assessment, namely the Sympatho-Vagal Synthetic Data Generation Model, combining cortical (electroencephalography, EEG) and peripheral (cardiac sympathovagal) neural dynamics. The causal, bidirectional neural control on heartbeat dynamics was quantified on data gathered from 26 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay sustained by EEG oscillations in the delta and gamma bands, primarily originating from sympathetic activity, whereas brain-to-heart interplay originates over central brain regions through sympathovagal control. The proposed methodology provides a viable computational tool for the functional assessment of the causal interplay between cortical and cardiac neural control

Functional assessment of bidirectional cortical and peripheral neural control on heartbeat dynamics: A brain-heart study on thermal stress

The study of functional Brain-Heart Interplay (BHI) from non-invasive recordings has gained much interest in recent years. Previous endeavors aimed at understanding how the two dynamical systems exchange information, providing novel holistic biomarkers and important insights on essential cognitive aspects and neural system functioning. However, the interplay between cardiac sympathovagal and cortical oscillations still has much room for further investigation. In this study, we introduce a new computational framework for a functional BHI assessment, namely the Sympatho-Vagal Synthetic Data Generation Model, combining cortical (electroencephalography, EEG) and peripheral (cardiac sympathovagal) neural dynamics. The causal, bidirectional neural control on heartbeat dynamics was quantified on data gathered from 26 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay sustained by EEG oscillations in the delta and gamma bands, primarily originating from sympathetic activity, whereas brain-to-heart interplay originates over central brain regions through sympathovagal control. The proposed methodology provides a viable computational tool for the functional assessment of the causal interplay between cortical and cardiac neural control

MEG analysis of neural dynamics in attention-deficit/hyperactivity disorder with fuzzy entropy

Producción CientíficaThe aim of this study was to analyze the neural dynamics in attention-deficit/hyperactivity disorder (ADHD). For this purpose, magnetoencephalographic (MEG) background activity was analyzed using fuzzy entropy (FuzzyEn), an entropy measure that quantifies signal irregularity, in 13 ADHD patients and 14 control children. Additionally, relative power (RP) was computed in conventional frequency bands (delta, theta, alpha, beta and gamma). FuzzyEn results showed that MEG activity was more regular in ADHD patients than in controls. Moreover, we found an increase of power in delta band and a decrease in the remaining frequency bands. Statistically significant differences (p-values <0.05; nonparametric permutation test for multiple comparisons) were detected for FuzzyEn in the posterior and left temporal regions, and for RP in the posterior, anterior and left temporal regions. Our results support the hypothesis that ADHD involves widespread functional brain abnormalities, affecting more areas than fronto-striatal circuits, such as the left temporal and posterior regions.Ministerio de Economía y Competitividad (project TEC2011-22987)Junta de Castilla y León (project BIO/VA38/14 and project VA059U13

C-Trend parameters and possibilities of federated learning

Abstract. In this observational study, federated learning, a cutting-edge approach to machine learning, was applied to one of the parameters provided by C-Trend Technology developed by Cerenion Oy. The aim was to compare the performance of federated learning to that of conventional machine learning. Additionally, the potential of federated learning for resolving the privacy concerns that prevent machine learning from realizing its full potential in the medical field was explored. Federated learning was applied to burst-suppression ratio’s machine learning and it was compared to the conventional machine learning of burst-suppression ratio calculated on the same dataset. A suitable aggregation method was developed and used in the updating of the global model. The performance metrics were compared and a descriptive analysis including box plots and histograms was conducted. As anticipated, towards the end of the training, federated learning’s performance was able to approach that of conventional machine learning. The strategy can be regarded to be valid because the performance metric values remained below the set test criterion levels. With this strategy, we will potentially be able to make use of data that would normally be kept confidential and, as we gain access to more data, eventually develop machine learning models that perform better. Federated learning has some great advantages and utilizing it in the context of qEEGs’ machine learning could potentially lead to models, which reach better performance by receiving data from multiple institutions without the difficulties of privacy restrictions. Some possible future directions include an implementation on heterogeneous data and on larger data volume.C-Trend-teknologian parametrit ja federoidun oppimisen mahdollisuudet. Tiivistelmä. Tässä havainnointitutkimuksessa federoitua oppimista, koneoppimisen huippuluokan lähestymistapaa, sovellettiin yhteen Cerenion Oy:n kehittämään C-Trend-teknologian tarjoamaan parametriin. Tavoitteena oli verrata federoidun oppimisen suorituskykyä perinteisen koneoppimisen suorituskykyyn. Lisäksi tutkittiin federoidun oppimisen mahdollisuuksia ratkaista yksityisyyden suojaan liittyviä rajoitteita, jotka estävät koneoppimista hyödyntämästä täyttä potentiaaliaan lääketieteen alalla. Federoitua oppimista sovellettiin purskevaimentumasuhteen koneoppimiseen ja sitä verrattiin purskevaimentumasuhteen laskemiseen, johon käytettiin perinteistä koneoppimista. Kummankin laskentaan käytettiin samaa dataa. Sopiva aggregointimenetelmä kehitettiin, jota käytettiin globaalin mallin päivittämisessä. Suorituskykymittareiden tuloksia verrattiin keskenään ja tehtiin kuvaileva analyysi, johon sisältyi laatikkokuvioita ja histogrammeja. Odotetusti opetuksen loppupuolella federoidun oppimisen suorituskyky pystyi lähestymään perinteisen koneoppimisen suorituskykyä. Menetelmää voidaan pitää pätevänä, koska suorituskykymittarin arvot pysyivät alle asetettujen testikriteerien tasojen. Tämän menetelmän avulla voimme ehkä hyödyntää dataa, joka normaalisti pidettäisiin salassa, ja kun saamme lisää dataa käyttöömme, voimme lopulta kehittää koneoppimismalleja, jotka saavuttavat paremman suorituskyvyn. Federoidulla oppimisella on joitakin suuria etuja, ja sen hyödyntäminen qEEG:n koneoppimisen yhteydessä voisi mahdollisesti johtaa malleihin, jotka saavuttavat paremman suorituskyvyn saamalla tietoja useista eri lähteistä ilman yksityisyyden suojaan liittyviä rajoituksia. Joitakin mahdollisia tulevia suuntauksia ovat muun muassa heterogeenisen datan ja suurempien tietomäärien käyttö

Hypoglycemia detection in patients with type 1 diabetes using EEG signals

The main risk for patients with type 1 diabetes (T1D) is to fall into hypoglycemia. We have extended the quantitative detection of hypoglycemia from the altered EEG signal in T1D patients by analyzing all EEG channel data through different measures of signal complexity such as the fractal domain and entropy indices. Finally, they were classified through a neural network in order to detect hypoglycemia with a high percentage of precision using the results obtained from the complexity analysis

An information theoretic learning framework based on Renyi’s α entropy for brain effective connectivity estimation

The interactions among neural populations distributed across different brain regions are at the core of cognitive and perceptual processing. Therefore, the ability of studying the flow of information within networks of connected neural assemblies is of fundamental importance to understand such processes. In that regard, brain connectivity measures constitute a valuable tool in neuroscience. They allow assessing functional interactions among brain regions through directed or non-directed statistical dependencies estimated from neural time series. Transfer entropy (TE) is one such measure. It is an effective connectivity estimation approach based on information theory concepts and statistical causality premises. It has gained increasing attention in the literature because it can capture purely nonlinear directed interactions, and is model free. That is to say, it does not require an initial hypothesis about the interactions present in the data. These properties make it an especially convenient tool in exploratory analyses. However, like any information-theoretic quantity, TE is defined in terms of probability distributions that in practice need to be estimated from data. A challenging task, whose outcome can significantly affect the results of TE. Also, it lacks a standard spectral representation, so it cannot reveal the local frequency band characteristics of the interactions it detects.Las interacciones entre poblaciones neuronales distribuidas en diferentes regiones del cerebro son el núcleo del procesamiento cognitivo y perceptivo. Por lo tanto, la capacidad de estudiar el flujo de información dentro de redes de conjuntos neuronales conectados es de fundamental importancia para comprender dichos procesos. En ese sentido, las medidas de conectividad cerebral constituyen una valiosa herramienta en neurociencia. Permiten evaluar interacciones funcionales entre regiones cerebrales a través de dependencias estadísticas dirigidas o no dirigidas estimadas a partir de series de tiempo. La transferencia de entropía (TE) es una de esas medidas. Es un enfoque de estimación de conectividad efectiva basada en conceptos de teoría de la información y premisas de causalidad estadística. Ha ganado una atención cada vez mayor en la literatura porque puede capturar interacciones dirigidas puramente no lineales y no depende de un modelo. Es decir, no requiere de una hipótesis inicial sobre las interacciones presentes en los datos. Estas propiedades la convierten en una herramienta especialmente conveniente en análisis exploratorios. Sin embargo, como cualquier concepto basado en teoría de la información, la TE se define en términos de distribuciones de probabilidad que en la práctica deben estimarse a partir de datos. Una tarea desafiante, cuyo resultado puede afectar significativamente los resultados de la TE. Además, carece de una representación espectral estándar, por lo que no puede revelar las características de banda de frecuencia local de las interacciones que detecta.DoctoradoDoctor(a) en IngenieríaContents List of Figures xi List of Tables xv Notation xvi 1 Preliminaries 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 Probability distribution estimation as an intermediate step in TE computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.2 The lack of a spectral representation for TE . . . . . . . . . . . . 7 1.3 Theoretical background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.1 Transfer entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.2 Granger causality . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3.3 Information theoretic learning from kernel matrices . . . . . . . . 12 1.4 Literature review on transfer entropy estimation . . . . . . . . . . . . . . 14 1.4.1 Transfer entropy in the frequency domain . . . . . . . . . . . . . . 17 1.5 Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5.1 General aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5.2 Specific aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.6 Outline and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.6.1 Kernel-based Renyi’s transfer entropy . . . . . . . . . . . . . . . . 24 1.6.2 Kernel-based Renyi’s phase transfer entropy . . . . . . . . . . . . 24 1.6.3 Kernel-based Renyi’s phase transfer entropy for the estimation of directed phase-amplitude interactions . . . . . . . . . . . . . . . . 25 1.7 EEG databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Contents ix 1.7.1 Motor imagery . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.7.2 Working memory . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.8 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2 Kernel-based Renyi’s transfer entropy 34 2.1 Kernel-based Renyi’s transfer entropy . . . . . . . . . . . . . . . . . . . . 35 2.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.1 VAR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.2 Modified linear Kus model . . . . . . . . . . . . . . . . . . . . . . 38 2.2.3 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.2.4 Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.3.1 VAR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.3.2 Modified linear Kus model . . . . . . . . . . . . . . . . . . . . . . 46 2.3.3 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 2.3.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3 Kernel-based Renyi’s phase transfer entropy 60 3.1 Kernel-based Renyi’s phase transfer entropy . . . . . . . . . . . . . . . . 61 3.1.1 Phase-based effective connectivity estimation approaches considered in this chapter . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.2.1 Neural mass models . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.2.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 3.2.3 Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.1 Neural mass models . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.3.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.3.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4 Kernel-based Renyi’s phase transfer entropy for the estimation of directed phase-amplitude interactions 84 4.1 Kernel-based Renyi’s phase transfer entropy for the estimation of directed phase-amplitude interactions . . . . . . . . . . . . . . . . . . . . . . . . . 85 x Contents 4.1.1 Transfer entropy for directed phase-amplitude interactions . . . . 85 4.1.2 Cross-frequency directionality . . . . . . . . . . . . . . . . . . . . 85 4.1.3 Phase transfer entropy and directed phase-amplitude interactions 86 4.2 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 4.2.1 Simulated phase-amplitude interactions . . . . . . . . . . . . . . . 88 4.2.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.2.3 Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . 91 4.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4.3.1 Simulated phase-amplitude interactions . . . . . . . . . . . . . . . 92 4.3.2 EEG data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.3.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5 Final Remarks 100 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.3 Academic products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.1 Journal papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.2 Conference papers . . . . . . . . . . . . . . . . . . . . . . . . . . 105 5.3.3 Conference presentations . . . . . . . . . . . . . . . . . . . . . . . 105 Appendix A Kernel methods and Renyi’s entropy estimation 106 A.1 Reproducing kernel Hilbert spaces . . . . . . . . . . . . . . . . . . . . . . 106 A.1.1 Reproducing kernels . . . . . . . . . . . . . . . . . . . . . . . . . 106 A.1.2 Kernel-based learning . . . . . . . . . . . . . . . . . . . . . . . . . 107 A.2 Kernel-based estimation of Renyi’s entropy . . . . . . . . . . . . . . . . . 109 Appendix B Surface Laplacian 113 Appendix C Permutation testing 115 Appendix D Kernel-based relevance analysis 117 Appendix E Cao’s criterion 120 Appendix F Neural mass model equations 122 References 12

Deep Learning for Electrophysiological Investigation and Estimation of Anesthetic-Induced Unconsciousness

Neuroscience has made a number of advances in the search for the neural correlates of consciousness, but our understanding of the neurophysiological markers remains incomplete. In this work, we apply deep learning techniques to resting-state electroencephalographic (EEG) measures of healthy participants under general anesthesia, for the investigation and estimation of altered states of consciousness. Specifically, we focus on states characterized by different levels of unconsciousness and anesthetic depths, based on definitions and metrics from contemporary clinical practice. Our experiments begin by exploring the ability of deep learning to extract relevant electrophysiological features, under a cross-subject decoding task. As there is no state-of-theart model for EEG analysis, we compare two widely used deep learning architectures - convolutional neural networks (cNNs) and multilayer perceptrons (MLPs) - and show that cNNs perform effectively, using only one second of the raw EEG signals. Relying on cNNs, we derive a novel 3D architecture design and a standard preprocessing pipeline, which allows us to exploit the spatio-temporal structure of the EEG, as well as to integrate different acquisition systems and datasets under a common methodology. We then focus on the nature of different predictive tasks, by investigating classification and regression algorithms under a variety of clinical ground-truths, based on behavioral, pharmacological, and psychometrical evidence for consciousness. Our findings provide several insights regarding the interaction across the anesthetic states, the electrophysiological signatures, and the temporal dynamics of the models. We also reveal an optimal training strategy, based on which we can detect progressive changes in levels of unconsciousness, with higher granularity than current clinical methods. Finally, we test the generalizability of our deep learning-based EEG framework, across subjects, experimental designs, and anesthetic agents (propofol, ketamine and xenon). Our results highlight the capacity of our model to acquire appropriate, task-related, cross-study features, and the potential to discover common cross-drug features of unconsciousness. This work has broader significance for discovering generalized electrophysiological markers that index states of consciousness, using a data-driven analysis approach. It also provides a basis for the development of automated, machine-learning driven, non-invasive EEG systems for real-time monitoring of the depth of anesthesia, which can advance patients' comfort and safety