DEVELOPMENT OF AN ACCURATE SEIZURE DETECTION SYSTEM USING RANDOM FOREST CLASSIFIER WITH ICA BASED ARTIFACT REMOVAL ON EEG DATA

Abstract The creation of a reliable artifact removal and precise epileptic seizure identification system using Seina Scalp EEG data and cutting-edge machine learning techniques is presented in this paper. Random Forest classifier used for seizure classification, and independent component analysis (ICA) is used for artifact removal. Various artifacts, such as eye blinks, muscular activity, and environmental noise, are successfully recognized and removed from the EEG signals using ICA-based artifact removal, increasing the accuracy of the analysis that comes after. A precise distinction between seizure and non-seizure segments is made possible by the Random Forest Classifier, which was created expressly to capture the spatial and temporal patterns associated with epileptic seizures. Experimental evaluation of the Seina Scalp EEG Data demonstrates the excellent accuracy of our approach, achieving a 96% seizure identification rate A potential strategy for improving the accuracy and clinical utility of EEG-based epilepsy diagnosis is the merging of modern signal processing methods and deep learning algorithms

A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks

BACKGROUND: The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment. METHODS: A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group. RESULTS:The study results show the existence of a statistically significant difference (p \u3c 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis. CONCLUSIONS:The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed

Performance metrics for the accurate characterisation of interictal spike detection algorithms.

Accepted versio

Feature Detection and Curve Fitting Using Fast Walsh Transforms for Shock Tracking: Applications

Walsh functions form an orthonormal basis set consisting of square waves. Square waves make the system well suited for detecting and representing functions with discontinuities. Given a uniform distribution of 2p cells on a one-dimensional element, it has been proven that the inner product of the Walsh Root function for group p with every polynomial of degree < or = (p - 1) across the element is identically zero. It has also been proven that the magnitude and location of a discontinuous jump, as represented by a Heaviside function, are explicitly identified by its Fast Walsh Transform (FWT) coefficients. These two proofs enable an algorithm that quickly provides a Weighted Least Squares fit to distributions across the element that include a discontinuity. The detection of a discontinuity enables analytic relations to locally describe its evolution and provide increased accuracy. Time accurate examples are provided for advection, Burgers equation, and Riemann problems (diaphragm burst) in closed tubes and de Laval nozzles. New algorithms to detect up to two C0 and/or C1 discontinuities within a single element are developed for application to the Riemann problem, in which a contact discontinuity and shock wave form after the diaphragm bursts

Intracranial Volume Estimation and Graph Theoretical Analysis of Brain Functional Connectivity Networks

Understanding pathways of neurological disorders requires extensive research on both functional and structural characteristics of the brain. This dissertation introduced two interrelated research endeavors, describing (1) a novel integrated approach for constructing functional connectivity networks (FCNs) of brain using non-invasive scalp EEG recordings; and (2) a decision aid for estimating intracranial volume (ICV). The approach in (1) was developed to study the alterations of networks in patients with pediatric epilepsy. Results demonstrated the existence of statistically significant (

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763)

EpiGauss : caracterização espacio-temporal da actividade cerebral em epilepsia

Doutoramento em Engenharia ElectrotécnicaA epilepsia é uma patologia cerebral que afecta cerca de 0,5% da população mundial. Nas epilepsias focais, o principal objectivo clínico é a localização da zona epileptogénica (área responsável pelas crises), uma informação crucial para uma terapêutica adequada. Esta tese é centrada na caracterização da actividade cerebral electromagnética do cérebro epiléptico. As contribuições nesta área, entre a engenharia e neurologia clínica, são em duas direcções. Primeiro, mostramos que os conceitos associados às pontas podem ser imprecisos e não ter uma definição objectiva, tornando necessária uma reformulação de forma a definir uma referência fiável em estudos relacionados com a análise de pontas. Mostramos que as características das pontas em EEG são estatisticamente diferentes das pontas em MEG. Esta constatação leva a concluir que a falta de objectividade na definição de ponta na literatura pode induzir utilizações erradas de conceitos associados ao EEG na análise de MEG. Também verificamos que o uso de conjuntos de detecções de pontas efectuadas por especialistas (MESS) como referência pode fornecer resultados enganadores quando apenas baseado em critérios de consenso clínico, nomeadamente na avaliação da sensibilidade e especificidade de métodos computorizados de detecção de pontas Em segundo lugar, propomos o uso de métodos estatísticos para ultrapassar a falta de precisão e objectividade das definições relacionadas com pontas. Propomos um novo método de neuroimagem suportado na caracterização de geradores electromagnéticos – EpiGauss – baseado na análise individual dos geradores de eventos do EEG que explora as suas estruturas espacio-temporais através da análise de “clusters”. A aplicação de análise de “clusters” à análise geradores de eventos do EEG tem como objectivo usar um método não supervisionado, para encontrar estruturas espacio-temporais dps geradores relevantes. Este método, como processo não supervisionado, é orientado a utilizadores clínicos e apresenta os resultados sob forma de imagens médicas com interpretação similar a outras técnicas de imagiologia cerebral. Com o EpiGauss, o utilizador pode determinar a localização estatisticamente mais provável de geradores, a sua estabilidade espacial e possíveis propagações entre diferente áreas do cérebro. O método foi testado em dois estudos clínicos envolvendo doentes com epilepsia associada aos hamartomas hipotalâmicos e o outro com doentes com diagnóstico de epilepsia occipital. Em ambos os estudos, o EpiGauss foi capaz de identificar a zona epileptogénica clínica, de forma consistente com a história e avaliação clínica dos neurofisiologistas, fornecendo mais informação relativa à estabilidade dos geradores e possíveis percursos de propagação da actividade epileptogénica contribuindo para uma melhor caracterização clínica dos doentes. A conclusão principal desta tese é que o uso de técnicas não supervisionadas, como a análise de “clusters”, associadas as técnicas não-invasivas de EMSI, pode contribuir com um valor acrescido no processo de diagnóstico clínico ao fornecer uma caracterização objectiva e representação visual de padrões complexos espaciotemporais da actividade eléctrica epileptogénica.Epilepsy is a brain pathology that affects 0.5% of the world population. In focal epilepsies, the main clinical objective is the localization of the epileptogenic zone (brain area responsible for the epileptic seizures – EZ), a key information to decide an adequate therapeutic approach. This thesis is centred on electromagnetic activity characterization of the epileptic brain. Our contribution to this boundary area between engineering and clinical neurology is two-folded. First we show that spike related clinical concepts can be unprecise and some do not have objective definitions making necessary a reformulation in order to have a reliable reference in spike related studies. We show that EEG spike wave quantitative features are statistically different from their MEG counterparts. This finding leads to the conclusion that the lack of objective spike feature definitions in the literature can induce the wrong usage of EEG feature definition in MEG analysis. We also show that the use of multi-expert spike selections sets (MESS) as gold standard, although clinically useful, may be misleading whenever defined solely in terms of clinical agreement criteria, namely as references for automatic spike detection algorithms in sensitivity and specificity method analysis. Second, we propose the use of statistical methods to overcome some lack of precision and objectivity in spike related definitions. In this context, we propose a new ElectroMagnetic Source Imaging (EMSI) method – EpiGauss – based on cluster analysis that explores both spatial and temporal information contained in individual events sources analysis characterisation. This automatic cluster method for the analysis of spike related electric generators based in EEG is used to provide an unsupervised tool to find their relevant spatio-temporal structures. This method enables a simple unsupervised procedure aimed for clinical users and presents its results in an intuitive representation similar to other brain imaging techniques. With EpiGauss, the user is able to determine statistically probable source locations, their spatial stability and propagation patterns between different brain areas. The method was tested in two different clinical neurophysiology studies, one with a group of Hypothalamic Hamartomas and another with a group of Occipital Epilepsy patients. In both studies EpiGauss identified the clinical epileptogenic zone, consistent with the clinical background and evaluation of neurophysiologists, providing further information on stability of source locations and their probable propagation pathways that enlarges their clinical interpretation. This thesis main conclusion is that the use of unsupervised techniques, such as clustering, associated with EMSI non-invasive techniques, can bring an added value in clinical diagnosis process by providing objective and visual representation of complex epileptic brain spatio-temporal activity patterns

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well