Absolute Epilepsy And Eeg Rotation Review

This book contains the most essential information needed for an epilepsy/ EEG rotation. Chapters are formatted with bullet points and feature clinical pearls. Concise and easy-to-read, this quick reference provides neurology residents, clinical neurophysiology and epilepsy fellows, and other clinicians with the most critical information in epilepsy and EEG in a simplified, yet comprehensive format. Divided into two sections, the book first covers the diagnosis, characteristics, and treatment of epilepsy. The second section focuses on EEG placement, procedures, and patterns in various neurological disorders.

This book uncovers stakes and possibilities offered by Computational Intelligence and Predictive Analytics to Medical Science. The main focus is on data technologies,classification, analysis and mining, information retrieval, and in the algorithms needed to elaborate the informations. A section with use cases and applications follows the two main parts of the book, respectively dedicated to the foundations and techniques of the discipline.

There are more than 80 different sleep disorders including insomnia, sleep apnea, restless leg syndrome, hypersomnia, circadian rhythm disorders, and parasomnia. Good sleep is necessary for optimal health and can affect hormone levels and weight. The use of artificial intelligence (AI) and biomedical signals and images can help in healthcare diagnostics that are related to these and other sleep disorders. Advancing the Investigation and Treatment of Sleep Disorders Using AI presents an overview of sleep disorders based on machine intelligence methods in order to learn and explore the latest advancements, developments, methods, systems, futuristic approaches, and algorithms towards sleep disorders and to address their challenges. This book also discusses recent and future advancements in various feature extraction techniques and machine learning methods. Covering topics such as biomedical signal processing, augmented reality for clinical investigation, and sleep disorder detection, this book is essential for sleep medicine practitioners, clinical psychologists, psychiatrists, medical technologists, doctors, IT specialists, biomedical engineers, researchers, graduate students, and academicians.

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems.

Deep Learning for EEG-Based Brain-Computer Interfaces is an exciting book that describes how emerging deep learning improves the future development of Brain-Computer Interfaces (BCI) in terms of representations, algorithms and applications. BCI bridges humanity's neural world and the physical world by decoding an individuals' brain signals into commands recognizable by computer devices.This book presents a highly comprehensive summary of commonly-used brain signals; a systematic introduction of around 12 subcategories of deep learning models; a mind-expanding summary of 200+ state-of-the-art studies adopting deep learning in BCI areas; an overview of a number of BCI applications and how deep learning contributes, along with 31 public BCI data sets. The authors also introduce a set of novel deep learning algorithms aimed at current BCI challenges such as robust representation learning, cross-scenario classification, and semi-supervised learning. Various real-world deep learning-based BCI applications are proposed and some prototypes are presented. The work contained within proposes effective and efficient models which will provide inspiration for people in academia and industry who work on BCI.Related Link(s)

This book involves the design, analysis, and application of various cognitive predictive maintenance tests with the help of tools like vibration analysis, ultrasonic analysis, infrared analysis, oil analysis, laser-shaft alignment, and motor circuit analysis in the prediction of various cognitive diseases such as epilepsy, Parkinson’s disease, Alzheimer’s disease, and depression. These are needed since there are no proper medical tests available to predict these diseases in remote areas at an early stage. Various emerging technologies are analyzed for the design of tests. Key features: Incorporates innovative processes for treating cognitive diseases. Early and exact identification and treatment strategies are incorporated. Future technologies like artificial intelligence, machine learning, the IoT, and data science are used to find solutions. Analysis with existing cognitive disease solutions is incorporated and simulations provided. The novelty of the book lies in the accurate prediction of cognitive diseases. Encompassing future technologies and various communication protocols or devices available for cognitive diseases for the design of new equipment are an outcome of the book. Various parameters like power consumption, productivity, and safety should be taken into account during the analysis, design, and application of a product. The book could well be added to the curriculum of medical colleges and biomedical engineering students. Possible vendors include biomedical research centers like Biotechnika and the Indian Council of Medical Research (ICMR). It would be a breakthrough for biomedical companies to launch their new products.

This is a first of its kind, comprehensive compendium for pediatrics practical examination, with frequently asked questions and case sheet formats. It is structured to help medical students to prepare in an intense framework that mimics the examination. It also aids in untangling complex topics like ventilator graphics, ECG, and X-rays with appended images. The book will prove invaluable to postgraduates and practicing pediatricians who require clear and systematic approach to clinical cases. Prototype case sheet for long and short cases - Detailed case analysis, encompassing history elicitation to counseling and prognosis - Case-based discussion with frequently asked questions (similar to viva voce) - Coverage of MD as well as DNB topics - Core topics that are complex, such as ECG, X-ray, EEG, and CT/MRI, are dealt in a simple manner - Ready reckoner for practical examinations

Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer’s disease and Parkinson’s disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients. As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Discusses longitudinal big data and risk factors surrounding the development of psychiatric disorders Analyzes methods in using big data to treat psychiatric and neurological disorders Describes the role machine learning can play in the analysis of big data Demonstrates the various methods of gathering big data in medicine Reviews how to apply big data to genetics