Download or read book Seizure Forecasting and Detection Computational Models Machine Learning and Translation into Devices written by Sharon Chiang and published by Frontiers Media SA. This book was released on 2022-03-31 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Translational Machine Learning for Epilepsy Therapy written by Steven Baldassano and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Continuous medical data monitoring is playing an increasingly important role in patient care, both in and out the hospital. Diagnosing and treating patients with epilepsy is especially reliant on continuous EEG monitoring to identify and respond to seizures. However, as use of continuous EEG becomes more common, both for long-term inpatient monitoring and in ambulatory or implanted devices, the burden of study interpretation is rapidly outpacing available physician resources. In particular, the advent of implanted neuroresponsive devices for treating medically-refractory epilepsy is generating large, streaming datasets potentially lasting for several years and containing hundreds of seizures. The current need for manual review of long-term, continuous EEG data introduces tremendous health care costs and can result in significant delays in seizure diagnosis and treatment. Automated data processing is essential to improve data usage, accurately and rapidly detect seizures, and provide scalability in clinical practice. This thesis aims to develop platforms for automated data analysis and event detection using custom machine learning algorithms for application in the intensive care unit and in implanted neural devices. The work presented in this thesis progresses through the development of each component of an automated data analysis platform. The first section describes a system for real-time data analysis and caretaker notification in the ICU, with a focus on the process necessary to harness multi-modal data from clinical recording sources. The next section details the process of developing machine learning algorithms for seizure detection. In this section, I present novel seizure detection strategies as well as a competition designed to crowdsource algorithm development. This work produced several highly-accurate, open-source seizure detection methods, validated in extended human implanted device data, along with pipelines to facilitate algorithm application and benchmarking in new datasets. The last section covers the integration of data management and seizure detection for implementation in next-generation medical devices. I present a novel paradigm to leverage cloud computing resources for seizure detection in an implanted device. This system is then implemented in vivo using a canine epilepsy model, with real-time seizure detection on streaming data from Medtronic's RC+S neurostimulating device. These algorithms and flexible analysis platforms are a step toward automating analysis of EEG data for epilepsy therapy. It is my hope that such systems will improve medical data usage, reshape caretaker workflow, and increase the clinical power of continuous medical monitoring.

Download or read book Machine Learning driven Patient specific Early Seizure Detection for Neuromodulation Devices written by Jamie Michael Koerner and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Epilepsy is a chronic disorder of the brain that predisposes individuals to experiencing recurrent and unprovoked seizures affecting 50 million people worldwide. Recent advances in fundamental neuroscience and implantable electronics have enabled the development of neuromodulation devices for the treatment of epilepsy. Modern neuromodulation devices detect abnormal electrical activity in the brain associated with seizures and activate electrical stimulation to prevent seizures from occurring. Today, there is a growing trend towards integrating machine learning for seizure detection on such devices to improve their efficacy. This thesis assesses the suitability of current machine learning models for neuromodulation devices by evaluating their seizure detection performance and efficiency in resource-constrained environments. Particular emphasis is placed on comparing traditional machine learning to modern deep learning models. This thesis will show that, in the seizure detection context, deep learning models can be implemented in a compact and resource-efficient way despite their computational complexity.

Download or read book Epileptic Seizure Prediction Using Electroencephalogram Signals written by Ratnaprabha Ravindra Borhade and published by CRC Press. This book was released on 2024-12-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an innovative method of EEG-based feature extraction and classification of seizures using EEG signals. It describes the methodology required for EEG analysis, seizure detection, seizure prediction and seizure classification. It contains a compilation of all techniques used in the literature and emphasises on newly proposed techniques. The book concentrates on a brief discussion of existing methods for epileptic seizure diagnosis and prediction, and introduces new efficient methods specifically for seizure prediction. * Focuses on the mathematical models and machine learning algorithms from a perspective of clinical deployment of EEG-based Epileptic Seizure Prediction * Discusses recent trends in seizure detection, prediction and classification methodologies * Provides engineering solutions to severity or risk analysis of detected seizures at remote places * Presents wearable solutions to seizure prediction * Includes details of the use of deep learning for Epileptic Seizure Prediction using EEG This book acts as a reference for academicians and professionals who are working in the field of Computational Biomedical Engineering and are interested in the domain of EEG-based disease prediction.

Download or read book Recent Advances In Predicting And Preventing Epileptic Seizures Proceedings Of The 5th International Workshop On Seizure Prediction written by Ronald Tetzlaff and published by World Scientific. This book was released on 2013-08-28 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computational Neuroscience in Epilepsy written by Ivan Soltesz and published by Academic Press. This book was released on 2011-09-02 with total page 649 pages. Available in PDF, EPUB and Kindle. Book excerpt: Epilepsy is a neurological disorder that affects millions of patients worldwide and arises from the concurrent action of multiple pathophysiological processes. The power of mathematical analysis and computational modeling is increasingly utilized in basic and clinical epilepsy research to better understand the relative importance of the multi-faceted, seizure-related changes taking place in the brain during an epileptic seizure. This groundbreaking book is designed to synthesize the current ideas and future directions of the emerging discipline of computational epilepsy research. Chapters address relevant basic questions (e.g., neuronal gain control) as well as long-standing, critically important clinical challenges (e.g., seizure prediction). Computational Neuroscience in Epilepsy should be of high interest to a wide range of readers, including undergraduate and graduate students, postdoctoral fellows and faculty working in the fields of basic or clinical neuroscience, epilepsy research, computational modeling and bioengineering. - Covers a wide range of topics from molecular to seizure predictions and brain implants to control seizures - Contributors are top experts at the forefront of computational epilepsy research - Chapter contents are highly relevant to both basic and clinical epilepsy researchers

Download or read book Seizure Prediction in Epilepsy written by Björn Schelter and published by John Wiley & Sons. This book was released on 2008-11-21 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Artificial Intelligence for Neurological Disorders written by Ajith Abraham and published by Academic Press. This book was released on 2022-09-23 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation. The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances. - Discusses various AI and ML methods to apply for neurological research - Explores Deep Learning techniques for brain MRI images - Covers AI techniques for the early detection of neurological diseases and seizure prediction - Examines cognitive therapies using AI and Deep Learning methods

Download or read book Deep Learning Models for Epileptic Seizure Detection and Prediction written by Ahmed Mohamed Barbary Abdelhameed and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Recent Advances in Predicting and Preventing Epileptic Seizures written by Ronald Tetzlaff and published by World Scientific Publishing Company Incorporated. This book was released on 2013 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book A Deep Learning Approach to Seizure Prediction with a Desirable Lead Time written by Yan Huang and published by . This book was released on 2019 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Epilepsy Innovation Institute (Ei2) 2016 community survey reported that unpredictability is the most challenging aspect for seizure management. Effective and precise prediction of epileptic seizure onset is now a fundamental computational challenge, and a quick, reliable, and accurate prediction within a shorter lead time can help patients and doctors to take effective actions before the seizure onset to reduce adverse outcomes. We automatically extract European Data Format (EDF) information and epilepsy annotation data from a cross-site source. A web-based annotation query interface is developed to build customized epilepsy datasets. We also introduce a two-phased approach which consists of a deep learning model with Long short-term memory (LSTM) neurons and a prediction memory queue. The LSTM model is applied to detect pre-ictal and inter-ictal periods from EEG signals, and the prediction memory queue is utilized to forecast seizures. Our predictor can output a warning for a potential seizure within a 5-minute lead time, which is significantly reduced from the 60 minute lead time used for the 2014 seizure prediction competition on kaggle.com. We construct patient-specific datasets and cohort-based datasets leveraging the Center for SUDEP Research's (CSR) large-scale labeled epilepsy electroencephalography (EEG) and electrocardiography (ECG) data. The evaluation shows that our model reached an average sensitivity of 95.24%, mean time in warning of 63.15%, and an improvement of 32.09% over a random predictor, which indicates that our deep learning approach is a promising direction for realistic seizure forecasting.

Download or read book Detection Simulation and Control in Models of Epilepsy written by Robert Durham Vincent and published by . This book was released on 2007 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Medical Image Analysis written by Alejandro Frangi and published by Academic Press. This book was released on 2023-09-20 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. - An authoritative presentation of key concepts and methods from experts in the field - Sections clearly explaining key methodological principles within relevant medical applications - Self-contained chapters enable the text to be used on courses with differing structures - A representative selection of modern topics and techniques in medical image computing - Focus on medical image computing as an enabling technology to tackle unmet clinical needs - Presentation of traditional and machine learning approaches to medical image computing

Download or read book A Comparison of Machine Learning Classifiers for Energy efficient Implementation of Seizure Detection written by Farrokh Manzouri and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Exploring Machine Learning Techniques in Epileptic Seizure Detection and Prediction written by Negin Moghim and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Components of Soft Computing for Epileptic Seizure Prediction and Detection written by Suguna Nanthini and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Components of soft computing include machine learning, fuzzy logic, evolutionary computation, and probabilistic theory. These components have the cognitive ability to learn effectively. They deal with imprecision and good tolerance of uncertainty. Components of soft computing are needed for developing automated expert systems. These systems reduce human interventions so as to complete a task essentially. Automated expert systems are developed in order to perform difficult jobs. The systems have been trained and tested using soft computing techniques. These systems are required in all kinds of fields and are especially very useful in medical diagnosis. This chapter describes the components of soft computing and review of some analyses regarding EEG signal classification. From those analyses, this chapter concludes that a number of features extracted are very important and relevant features for classifier can give better accuracy of classification. The classifier with a suitable learning method can perform well for automated epileptic seizure detection systems. Further, the decomposition of EEG signal at level 4 is sufficient for seizure detection.

Download or read book Advanced Biomedical Devices for Epileptic Seizure Detection and Prediction written by K. Ramesh and published by . This book was released on 2018 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: