EEG Signal Analysis for Automated Epilepsy Seizure Detection Using Wavelet Transform and Artificial Neural Network

Electroencephalogram (EEG) measures electrical activity of the brain and proffers valuable insight of the brain dynamics. Accurate and careful analysis of EEG signal plays a prominent role in the diagnosis of brain diseases like epilepsy, brain tumor. EEG is the most significant method used for epilepsy monitoring, diagnosis and rehabilitation. A patient-specific seizure detection model has been developed using Haar wavelet and Artificial Neural Network. HAAR Wavelet decomposition of multi-channel EEG with five scales is made and three frequency bands of EEG selected for the consequent process. The conventional Haar wavelet transform (HWT) is replaced by a modified Haar wavelet transform whereas the number of multiplications and additions are reduced. The Haar wavelet reduces computational complexity from the existing Haar wavelet structure which consumes only 1–3 ms based on the decomposition level to detect epilepsy.

Infrared Thermal Imaging for Diabetes Detection and Measurement

The diabetes mellititus (type 1) condition occur when the beta cell destroy partially due to autoimmune process. The beta cells produce insulin with respect to blood glucose level. The insulin hormone regulates blood glucose in body. The blood glucose increases in body when insulin secretion is low from pancreas, termed as Diabetes mellititus. The Diabetes mellititus causes infection, pain in mouth. The regions in mouth affected by diabetes mellititius include gums, teeth, jaw and tongue. The glucose level increases in saliva which grows harmful bacteria. The bacteria in combination with plaque cause bad breath, gum disease and coating on tongue. The coating and sugar level in tongue alters the temperature of tongue. In this paper we propose to analyze tongue thermal image to diagnose diabetes at early stage.