A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients

Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction

EEG Resting-State Brain Topological Reorganization as a Function of Age

Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated b y combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signal srecordedatrestfrom71healthysubjects(age:20–63years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Schizo-Net: A novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning on Electroencephalogram-Based Brain Connectivity Indices

Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain connectivity indices are estimated from the windowed EEG activity, and six different deep learning architectures (with varying neurons and hidden layers) are trained. The present study is the first which considers a large number of brain connectivity indices, especially for SCZ. A detailed study was also performed that identifies SCZ-related changes occurring in brain connectivity, and the vital significance of BCI is drawn in this regard to identify the biomarkers of the disease. Schizo-Net surpasses current models and achieves 99.84% accuracy. An optimum deep learning architecture selection is also performed for improved classification. The study also establishes that Late fusion technique outperforms single architecture-based prediction in diagnosing SCZ

Анализ взаимосвязи между центральной нервной и сердечно-сосудистой системами

В роботі розглянуто взаємозв’язок між центральною нервовою та серцево-судинними системами. Описані існуючі методи оцінки зв’язку між сигналами варіабельності серцевого ритму і електроенцефалограми людини: кореляція, когерентність, взаємна інформація, ентропія передачі, ймовірність синхронізації. Найбільш перспективними напрямами визнано дослідження нелінійного взаємозв’язку між розглянутими системами, розгляд методів оцінки нелінійного зв’язку між сигналами ЕЕГ та сигналами варіабельності серцевого ритму та їх порівняння. Визначені шляхи покращення існуючих підходів до даної задачі та напрямки подальших досліджень.In the paper the aspects of collaboration and interconnection between central nervous and cardiovascular systems are described. Existing methods to estimate the connectivity between HRV and EEG signals and corresponding up-to-date studies are reviewed. It can be affirmed that there is an apparent interconnection between central nervous and cardiovascular systems on the basis of examined papers. But the definite method of assessment of this interconnection capable to take into account the underlying manner of this connection is yet to be defined. It was determined that further research should be directed into examination of non-linear connectivity between HRV and EEG signals, methods for non-linear connectivity assessment and comparison of their performance. On this basis the new ways to improve the current approaches are expounded.В работе рассмотрена взаимосвязь между центральной нервной и сердечно-сосудистой системой. Описаны существующие методы оценки связи между сигналами вариабельности сердечного ритма и электроэнцефалограммы человека: корреляция, когерентность, взаимная информация, энтропия передачи, вероятность синхронизации. Установлены наиболее перспективные направления исследований: определение нелинейной взаимосвязи между рассмотренными системами, рассмотрение методов оценки нелинейной связи межу сигналами ЭЭГ и сигналами вариабельности сердечного ритма и их сравнение. Обозначены пути улучшения существующих подходов к данной задаче и направления последующих исследований

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability

Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data

The recent development of genetically encoded calcium indicators enables monitoring in vivo the activity of neuronal populations. Most analysis of these calcium transients relies on linear regression analysis based on the sensory stimulus applied or the behavior observed. To estimate the basic properties of the functional neural circuitry, we propose a network-based approach based on calcium imaging recorded at single cell resolution. Differently from previous analysis based on cross-correlation, we used Granger-causality estimates to infer activity propagation between the activities of different neurons. The resulting functional networks were then modeled as directed graphs and characterized in terms of connectivity and node centralities. We applied our approach to calcium transients recorded at low frequency (4 Hz) in ventral neurons of the zebrafish spinal cord at the embryonic stage when spontaneous coiling of the tail occurs. Our analysis on population calcium imaging data revealed a strong ipsilateral connectivity and a characteristic hierarchical organization of the network hubs that supported established propagation of activity from rostral to caudal spinal cord. Our method could be used for detecting functional defects in neuronal circuitry during development and pathological conditions

Information-theoretic approach for the characterization of interactions in nonlinear dynamical systems

Symbolic time series analysis provides us a solid and broadly used toolkit for the characterization of interactions between nonlinear dynamical systems. In this thesis, information-theoretic measures are evaluated with respect to their capability to characterize interactions between dynamical systems. We investigate several important limitations of these measures which may appear when experimental data exhibit strong correlations. It is demonstrated that a high degree of static and/or long-term temporal correlations can, in general, lead to the incorrect inference of directionality of interactions between underlying dynamical systems. In this thesis, we propose two complementary information-theoretic measures which can provide a better characterization of the directionality of interactions in cases where the influence of such correlations in data cannot be neglected. First, the proposed information-theoretic measures are applied to characterize interactions between dynamical model systems with known equations of motion. Finally, they are applied to characterize interactions between multi-channel electroencephalographic recordings from epilepsy patients undergoing the presurgical diagnostics.Informationstheoretischer Ansatz zur Charakterisierung von Interaktionen in nichtlinearen dynamischen Systemen Mit Hilfe der Zeitreihenanalyse können Interaktionen zwischen natürlichen dynamischen Systemen anhand experimenteller Daten charakterisiert werden. In den letzten Jahren wurde eine Reihe von Maßen vorgestellt, die darauf abzielen, neben der Interaktionsrichtung auch die Interaktionsstärke zu bestimmen. Die zur Charakterisierung von Interaktionsrichtungen konzipierte Transferentropie zeichnet sich gerade durch eine besonders hohe Rauschtoleranz gegenüber anderen Maßen aus. Ziel der vorliegenden Arbeit ist es, zwei Limitationen, die die Interpretierbarkeit der Charakterisierungen mit der bisher vorgeschlagenen Transferentropie einschränken, zu untersuchen und auszuräumen. Zum einen wird ein Verfahren entwickelt und implementiert, mit dem langreichweitige Korrelationen besser beobachtet werden können, zum anderen werden Korrekturen vorgeschlagen, die den Einfluss so genannter statischer Korrelationen berücksichtigen. Bei Charakterisierungen von Interaktionsrichtungen mit Hilfe der Transferentropie konnten langreichweitige Korrelationen nur durch die Abschätzung von hochdimensionalen Wahrscheinlichkeitsräumen berücksichtigt werden. Für diese Abschätzung sind sehr viele Datenpunkte innerhalb des Beobachtungsintervalls notwendig, was bei Felddaten, gemessen an unbekannten Systemen, mit der Annahme der Stationarität in einem Beobachtungsintervall konkurriert. Um diese Beschränkung zu umgehen, wird in dieser Dissertation eine Verallgemeinerung des Konzepts der Entropie im Sinne von Lempel-Ziv auf das Maß der Transferentropie übertragen. Hierdurch können langreichweitige Korrelationen ohne die Abschätzung eines hochdimensionalen Wahrscheinlichkeitsraums bestimmt werden. Zeitgleiche Korrelationen der zugrunde liegenden Signale - so genannte statische Korrelationen - können die Interpretierbarkeit der Charakterisierung einschränken. Zur Berücksichtigung statistischer Korrelationen mit den bisher vorgestellten Maßen war ebenfalls eine mit einem großen Rechenaufwand verbundene Abschätzung hochdimensionaler Wahrscheinlichkeiten notwendig. In der vorliegenden Dissertation wird eine Korrektur der Transferentropie zur Abschätzung der statischen Korrelationen vorgeschlagen, ohne höherdimensionale Terme berechnen zu müssen. Durch die in dieser Arbeit vorgestellten Maße und Korrekturen kann die Charakterisierung der Interaktionsrichtung verbessert werden. Dabei wird anhand prototypischer Modellsysteme mit chaotischen Dynamiken demonstriert, dass die Charakterisierungen mit Hilfe der vorgeschlagenen Maße und Korrekturen gerade bei Systemen, die ohne Zeitversatz interagieren, besser interpretierbar sind. Weiterhin wurden Interaktionsstärke und Interaktionsrichtung an Zeitreihen hirnelektrischer Aktivität von Epilepsiepatienten bestimmt und mit Charakterisierungen der Transferentropie verglichen. Hierbei lässt sich zusammenfassen, dass sich mit den in dieser Arbeit vorgestellten Maßen Kontraste unterschiedlicher Interaktionsrichtungen besser auflösen lassen

Dynamic imaging of coherent sources reveals different network connectivity underlying the generation and perpetuation of epileptic seizures

The concept of focal epilepsies includes a seizure origin in brain regions with hyper synchronous activity (epileptogenic zone and seizure onset zone) and a complex epileptic network of different brain areas involved in the generation, propagation, and modulation of seizures. The purpose of this work was to study functional and effective connectivity between regions involved in networks of epileptic seizures. The beginning and middle part of focal seizures from ictal surface EEG data were analyzed using dynamic imaging of coherent sources (DICS), an inverse solution in the frequency domain which describes neuronal networks and coherences of oscillatory brain activities. The information flow (effective connectivity) between coherent sources was investigated using the renormalized partial directed coherence (RPDC) method. In 8/11 patients, the first and second source of epileptic activity as found by DICS were concordant with the operative resection site; these patients became seizure free after epilepsy surgery. In the remaining 3 patients, the results of DICS / RPDC calculations and the resection site were discordant; these patients had a poorer post-operative outcome. The first sources as found by DICS were located predominantly in cortical structures; subsequent sources included some subcortical structures: thalamus, Nucl. Subthalamicus and cerebellum. DICS seems to be a powerful tool to define the seizure onset zone and the epileptic networks involved. Seizure generation seems to be related to the propagation of epileptic activity from the primary source in the seizure onset zone, and maintenance of seizures is attributed to the perpetuation of epileptic activity between nodes in the epileptic network. Despite of these promising results, this proof of principle study needs further confirmation prior to the use of the described methods in the clinical praxis