Determination of high performance wavelet type in eeg signals analysis

Bioelectrical signs, which are the results of the brain neural activity, are called Electroencephalogram (EEG). EEG signals play an important role in the diagnosis and study of many nervous diseases such as epilepsy. EEG signals, which provide important information about brain activity is also a non-invasive method. EEG signals, who do not have a specific waveforms, containing five frequency bands. These bands are called as alpha, beta, theta, delta and gamma. Spectral analysis of EEG signals help to extract information to diagnose diseases. Epileptic seizure is a situation that occurs depending on the sudden, excessive and abnormal discharge arise of brain cells can not be controlled. Epileptic seizures may be considered as an unexpected electrical impulse. In this study, discrete wavelet transform (DWT) was used for spectral analysis of EEG signals belonging to the epilepsy patients and healthy people due to the success of the DWT method in analysis of non-stationary signals like EEG. Wavelet decomposition level was set to 6 for this study. Detailed and approximate wavelet coefficients were calculated based on the six detailed and one approximate sub-bands. The attributes that define the EEG signal is given by the wavelet coefficients. Different wavelet types such as Daubechies, Coiflet, Symmlet and discrete Meyer were used in EEG signal analysis with the discrete wavelet transform. These analyses showed that every single wavelet and different degrees of the wavelets give changing results. According to the results, the type and the degree of the best performing wavelet is determined. Consequently, the analysis results have shown that Daubechies has been type of best performing wavelet and 44 has been it?s degree. Determination of the wavelet coefficients, and various statistical calculations of wavelet coefficients were made with Matlab software.