This book will assist in medical diagnosis and clinical decision making in cardiology. While grammar has long been used in the analysis of programming languages and to describe natural languages, the discipline can be extended to other areas of applications such as the ECG signal, the scintigraphic image and the tomographic image. Compared to statistical methods, the use of grammars in cardiology offers more flexibility in applications and can powerfully represent object structures, and therefore make information retrieval and medical diagnosis easier.

This book will assist medical diagnosis and aid clinical decision-making. It shows that neuronal networks, machine learning, and VAR models proved useful for EEG signal analysis, and details EEG signal processing methods. The book also highlights how to use learning machine for epileptic cerebral activity localisation, and details a parallel implementation for EEG artefact rejection.