The EEG signals are highly subjective and the information about the various states may appear at random in the time scale. For example, a time series may be obtained by recording at regular time intervals the mean electrical activity of a portion of the mammalian brain. More specifically, by using a time series one can determine the possibility of constructing an attractor and thereby establishing the deterministic character of dynamic underlying system. Such methods from the non linear dynamical theory can be dragged for better perception of EEG signals. The complexity of drowsiness...

This book emphasizes on a Field Programmable Gate Array (FPGA) Implementation of Fuzzy PD and PID Controller for biomedical application. A novel approach aims to identify and design a simple robust Fuzzy (PD and PID) Controller system with minimum number of fuzzy rules to deliver insulin from pumps as a single injection process for the diabetic patients. This controller is an automatic feedback control system in which the patients blood glucose level is monitored from non-invasive Photo plethysmogram of a pulse oximeter (Photoglucometer) and insulin is infused with the change in the glucose...

Digital Image Processing and Medical Image Processing present an exciting and dynamic part of cognitive and pattern recognition techniques. Diagnostic applications of medical images are very exciting and they throw more insight about segmentation algorithms. This monograph reflects an introductory methodology for medical image segmentation using k-means clustering and subsequent optimization of clusters by means of EM and SVD. Chapter 1 introduces the need for segmentation of medical images and focus of the research. Chapter 2 discusses the general clustering algorithm and chapter 3...

Epileptic seizures result from a sudden electrical disturbance to the brain. Approximately one in every 100 persons will experience a seizure at some time in their life. In this work, we propose a genetic algorithm, SVM based fuzzy knowledge integration framework that is used for classification of risk level of epilepsy in diabetic patients from Electroencephalogram (EEG) signals. A statistical analysis of the EEG signal to indicate the onset of epilepsy based on chi square tests and control limits. Ten known diabetic patients with raw EEG recording are studied. Chapter 1 introduces the...

Epilepsy is a common and diverse set of chronic neurological disorders characterized by seizures. It is a paroxysmal behavioral spell generally caused by an excessive disorderly discharge of cortical nerve cells of the brain. Epilepsy is marked by the term "epileptic seizures." Epileptic seizures result from abnormal, excessive or hyper-synchronous neuronal activity in the brain. About 50 million people worldwide have epilepsy, and nearly 80% of epilepsy occurs in developing countries. The most common way to interfere with epilepsy is to analyse the EEG (electroencephalogram) signal which is...

This project presents the performance analysis of Particle swarm optimization (PSO), hybrid PSO and Bayesian classifier to calculate the epileptic risk level from electroencephalogram (EEG) inputs. PSO is an optimization technique which is initialized with a population of random solutions and searches for optima by updating generations. PSO is initialized with a group of random particles (solutions) and then searches for optima by updating generations. Hybrid PSO differs from ordinary PSO by calculating inertia weight to avoid the local minima problem. Bayesian classifier works on the...