Güç spektral yoğunluğu yöntemi ile epileptik nöbet tespiti

Detection of pre-seizure signs in epileptic signals may help patients to survive the seizure with minimal damage. This thesis aims to detect the pre-seizure and the seizure patterns using different two databases. The first EEG dataset was provided by the Department of Epileptology, the University of Bonn, Germany (A, C, and E sets were used). The database collected at Boston Children's Hospital was used as the second data set. The power, frequency, and amplitude values of all EEG signals in this data set were examined and compared. The power spectral densities (PSD) of each signal were analyzed graphically. The maximum PSD of the signals and high-frequency oscillations (HFOs) in the signals are investigated. The frequency subbands of the maximum PSD are examined. It was determined that the maximum power at the time of seizure was in the delta and theta subbands (these subbands are pathologic). The maximum power of F4 and T3 channels for all patients included only delta and theta subbands and HFOs are detected in these channels. Furthermore, frequency increase rates of pre-ictal and ictal signals are investigated, and increasing PSDs of HFOs are then calculated. In addition, the frequency of signals is observed to be 80 Hz and above in the Fp2, C4, P4, O2, and Pz channels, which are common to all patients. Also, when the subbands with the maximum power before seizure were examined, it was determined that the T8-P8 and P7-T7 channels had alpha and beta subbands in most of the patients.Consequently, determined the differences between healthy, pre-seizure, and epileptic patterns.