Atlas of Intensive Care Quantitative EEG
Marcus C. Ng, MD, Jin Jing, PhD, M. Brandon Westover, MD, PhD
Atlas of Intensive Care Quantitative EEG is the first resource fully dedicated to quantitative EEG (QEEG) analysis, tailored to any physician or EEG technologist who works with critically ill patients. With the rise of continuous EEG monitoring in intensive care, clinicians are increasingly called on to make real-time clinical judgments with little formal guidance on how to interpret QEEG. This book is configured to meet daily practice challenges. It addresses not only technical fundamentals but also provides numerous examples of signature QEEG patterns and artifacts to instruct both untrained and experienced eyes.
Comprehensive in scope, this unique atlas walks the reader from essential principles all the way through to practical pattern recognition. With full-page reference samples pairing raw EEG with quantitative EEG spectrograms, brief clinical vignettes, and explanatory captions noting significant features, this book provides a roadmap for understanding and applying QEEG data in critically ill patients. Unrivaled in the breadth of its coverage and level of detail, its thorough discussions of both normal and abnormal findings and QEEG artifacts set the standard for effective use of quantitative electroencephalography and trend analysis in the ICU. Complete with a broad range of patterns and page after page of full-color samples, this book is designed to be the authoritative QEEG reference for neurologists, intensivists, technologists, and trainees working in critical care settings.
- Key Features:
- Includes full spectrum of abnormal ICU QEEG findings with multiple examples of each pattern to assist readers in recognizing the range of findings encountered in clinical practice
- Contains more than 400 full-page vivid color QEEG examples paired with raw EEG to build interpretive skills and enhance clinical decision-making
- Concise presentation of fundamental principles of QEEG
- Detailed analysis of QEEG artifacts that can be mistaken for abnormal findings