Journals Proceedings

Abstract

A significant portion of the population (0.5% - 0.8%) suffers from epilepsy. This study is an effort to predict seizures in epileptic patients. Electroencephalogram (EEG) continues to be an attractive tool in clinical practice due to its non-invasive nature and its real time depiction of brain function. The Short time Fourier Transform (STFT) is used for time frequency analysis of signals. In the present analysis, the authors adopted an effective technique for the denoising of EEG signals corrupted by non-stationary noises using Undecimated Wavelet Transform (UWT) which is implemented through Laboratory Virtual Instrumentation Engineering Workbench LabVIEW platform This Technique has overcome some of the short comings of the STFT by performing a multi - resolution analysis of signals. In the Wavelet Transform, high frequency components are analyzed with a sharp time resolution than low frequency components. This is the most desirable property, especially in analyzing fast transient waveforms such as EEG spikes. This paper deals with EEG spike detection based on wavelet representation using LabVIEW. The results obtained through this technique, show the superior performance of the Wavelet Transform Technique over the other techniques for detecting EEG spikes in terms of higher resolution and robust noise immunity.