A study of EEG feature complexity in epileptic seizure prediction

The purpose of this study is (1) to provide EEG feature complexity analysis in seizure prediction by inter-ictal and pre-ital data classification and, (2) to assess the between-subject variability of the considered features. In the past several decades, there has been a sustained interest in predicting epilepsy seizure using EEG data. Most methods classify features extracted from EEG, which they assume are characteristic of the presence of an epilepsy episode, for instance, by distinguishing a pre-ictal interval of data (which is in a given window just before the onset of a seizure) from inter-ictal (which is in preceding windows following the seizure). To evaluate the difficulty of this classification problem independently of the classification model, we investigate the complexity of an exhaustive list of 88 features using various complexity metrics, i.e., the Fisher discriminant ratio, the volume of overlap, and the individual feature efficiency. Complexity measurements on real and synthetic data testbeds reveal that that seizure prediction by pre-ictal/inter-ictal feature distinction is a problem of significant complexity. It shows that several features are clearly useful, without decidedly identifying an optimal set

Mathematical Modelling at IIASA

Much of IIASA's research in the first ten years of its existence has been concerned with the analysis of complex systems; some of these have been global in scale, such as the studies of energy or food and agriculture, while others, like the Lake Balaton study or the work on small open economies, have concentrated on individual smaller systems. Mathematical modelling has played an important role in all of these analyses. Not only does this approach provide a simplified representation of real-world systems, allowing the modeller to study and sometimes even predict the behavior of the system, but also it can be used for policy analysis and planning purposes. To support these many and varied applications, IIASA has had to make significant advances in modelling methodology, which have allowed the modelling activities to develop in new directions. In view of IIASA's contributions to the theory and the practice of mathematical modelling, therefore, it is particularly appropriate that a collection of papers by IIASA researchers should have been published as a special issue of "Mathematical Modelling." These seven papers give some idea of the range of IIASA's modelling activities, but by no means represent the full scope of IIASA's research program. They have been selected for their methodological or practical relevance to the art of mathematical modelling

Uncertainty and Forecasting of Water Quality

This book brings together a number of critical discussions on the role of uncertainty in the development and use of mathematical models for water quality management. It covers the application of recursive estimation, time-series analysis, maximum likelihood estimation, and the Group Method of Data Handling (GMDH), to the problem of model identification. It also treats the analysis of prediction-error propagation, real-time forecasting, and the use of Monte Carlo simulation in the generation of speculative hypotheses about system behaviour

Mathematical Modeling of Water Quality: Streams, Lakes and Reservoirs

This book is the first to deal comprehensively with the subject of mathematical modeling of water quality in streams, lakes, and reservoirs. About one third of the book is devoted to model development processes -- identification, formulation, parameter estimation, calibration, sensitivity testing, and application -- and a thorough review of the mathematical principles and techniques of modeling. Emphasis is placed on well documented models, representative of the current state of the art, to illustrate capabilities and limitations for the simulation of water quality. About two thirds of the book deals with specific applications of models for simulation of water quality in natural water bodies. Topics covered include modeling of temperature, dissolved oxygen and phytoplankton growth in streams, development and application of one-dimensional models of stratified impoundments, two- and three-dimensional modeling of circulation and water quality in large lakes, thermally stratified plumes and cooling ponds, ecology of lakes and reservoirs, modeling of toxic substances, and the use of models in water quality management and decision making