Journals Proceedings

Abstract

The biological signals such as EEG, ECG are non stationary in deterministic signals. The processing of these signals requires special analysis and computations. The chaoticity of these signals are analyzed. This result can be used as feature spaces for diagnosis of various diseases. In this paper, the EEG signals of epileptic patients are analyzed using wavelets and are provided with user interface. Epilepsy is a chronic neurological disorder in brain which results in high spikes. The noise in raw EEG signals is eliminated by applying optimal FIR filter. These signals can then be finely separated into different frequency bands and analyzed instead of taking as a whole for efficient diagnosis of diseases. A novel approach for the sub band separation of EEG signals for analysis is done using wavelet packet transforms. Wavelet transforms are done to obtain time frequency localization of the signal. The signal is separated into its alpha, beta, gamma, theta and delta sub bands. This gives good temporal resolution than Fourier transforms. The scaling and wavelet functions are applied on the input signal. This is mainly useful in clinical applications to view fine tuned results. The results are analyzed using a suitable graphical user interface.