Comprising some 30 contributions, experts from around the world present and discuss recent advances related to seizure prediction in epilepsy. The book covers an extraordinarily broad spectrum, starting from modeling epilepsy in single cells or networks of a few cells to precisely-tailored seizure prediction techniques as applied to human data. This unique overview of our current level of knowledge and future perspectives provides theoreticians as well as practitioners, newcomers and experts with an up-to-date survey of developments in this important field of research.
A study of epilepsy from an engineering perspective, this volume begins by summarizing the physiology and the fundamental ideas behind the measurement, analysis and modeling of the epileptic brain. It introduces the EEG and provides an explanation of the type of brain activity likely to register in EEG measurements, offering an overview of how these EEG records are and have been analyzed in the past. The book focuses on the problem of seizure detection and surveys the physiologically based dynamic models of brain activity. Finally, it addresses the fundamental question: can seizures be predicted? Based on the authors' extensive research, the book concludes by exploring a range of future possibilities in seizure prediction.
This book is to improve our understanding of mechanisms leading to seizures in humans and in developing new therapeutic options. The book covers topics such as recent approaches to seizure control, recent developments in signal processing of interest for seizure prediction, ictogenesis in complex epileptic brain networks, active probing of the pre-seizure state, non-EEG based approaches to the transition to seizures, microseizures and their role in the generation of clinical seizures, the impact of sleep and long-biological cycles on seizure prediction, as well as animal and computational models of seizures and epilepsy. Furthermore the book covers recent developments of international databases and of parallel computing structures based on Cellular Nonlinear Networks that can play an important role in the realization of a portable seizure warning device.
As a truly translational area of biomedical investigation, epilepsy research spans an extraordinary breadth of subjects and involves virtually every tool that modern neuroscience has at its disposal. The Encyclopedia of Basic Epilepsy Research provides an up to date, comprehensive reference for all epilepsy researchers. With an expert list of authors, the encyclopedia covers the full spectrum of research activities from genes and molecules to animal models and human patients. The encyclopedia's electronic format also provides unparalleled access to frequent updates and additions, while the limited edition print version provides another option for owning this content. The Encyclopedia of Basic Epilepsy Research is an essential resource for researchers of all levels and clinicians who study epilepsy. The only comprehensive reference for basic research and current activities in epilepsy Electronic format provides fast and easy access to updates and additions, with limited print version available as well Contains over 85 articles, all written by experts in epilepsy research
Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. Provides an authoritative description of key concepts and methods Includes tutorial-based sections that clearly explain principles and their application to different medical domains Presents a representative selection of topics to match a modern and relevant approach to medical image computing
Models of Seizures and Epilepsy, Second Edition, is a valuable, practical reference for investigators who are searching for the most appropriate laboratory models to address key questions in the field. The book also provides an important background for physicians, fellows, and students, offering insight into the potential for advances in epilepsy research as well as R&D drug development. Contents include the current spectrum of models available to model different epilepsy syndromes, epilepsy in transgenic animals, comorbidities in models of epilepsy, and novel technologies to study seizures and epilepsies in animals. Provides a comprehensive reference detailing animal models of epilepsy and seizure Offers insights on the use of novel technologies that can be applied in experimental epilepsy research Edited by leading experts in the field that provide not only technical reviews of these models but also conceptual critiques Comments on the strengths and limitations of various models, including their relationship to clinical phenomenology and their value in developing better understanding and treatments
Brain Seizure Detection and Classification Using Electroencephalographic Signals presents EEG signal processing and analysis with high performance feature extraction. The book covers the feature selection method based on One-way ANOVA, along with high performance machine learning classifiers for the classification of EEG signals in normal and epileptic EEG signals. In addition, the authors also present new methods of feature extraction, including Singular Spectrum-Empirical Wavelet Transform (SSEWT) for improved classification of seizures in significant seizure-types, specifically epileptic and Non-Epileptic Seizures (NES). The performance of the system is compared with existing methods of feature extraction using Wavelet Transform (WT) and Empirical Wavelet Transform (EWT). The book's objective is to analyze the EEG signals to observe abnormalities of brain activities called epileptic seizure. Seizure is a neurological disorder in which too many neurons are excited at the same time and are triggered by brain injury or by chemical imbalance. Presents EEG signal processing and analysis concepts with high performance feature extraction Discusses recent trends in seizure detection, prediction and classification methodologies Helps classify epileptic and non-epileptic seizures where misdiagnosis may lead to the unnecessary use of antiepileptic medication Provides new guidance and technical discussions on feature-extraction methods and feature selection methods based on One-way ANOVA, along with high performance machine learning classifiers for classification of EEG signals in normal and epileptic EEG signals, and new methods of feature extraction developed by the authors, including Singular Spectrum-Empirical Wavelet
The first book to bring together experts in epilepsy, bio-engineering and dynamical systems theory, to discuss the possibility of treating epilepsy by controlling mechanisms that cause seizures. It includes a unique reference database with more than 1000 references, including hard-to-access references in the Russian literature.
This open access book constitutes the refereed proceedings of the 17th International Conference on String Processing and Information Retrieval, ICOST 2019, held in New York City, NY, USA, in October 2019. The 15 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 24 submissions. They cover topics such as: e-health technology design; well-being technology; biomedical and health informatics; and smart environment technology.
