Epileptic seizure detection combining power spectral density and high-frequency oscillations - Rabia TUTUK; Reyhan ZENGIN (International Journal of Applied Methods in Electronics and Computers, 2023)
Detection of pre-seizure signs in epileptic signals may help patients to survive the seizure with minimal damage. This study aims to detect epileptic seizure patterns using EEG datasets of five patients. The signals’ maximum power spectral density (PSD) and high-frequency oscillations (HFOs) signals are investigated. The PSDs of all patients’ signals are calculated, and the subbands of the maximum PSD are examined. It is observed that 95%, 85%, 85%, 75%, and 80% of the channels of the five patients are in the sum of delta and theta subbands of the maximum PSD, respectively. All patients’ maximum power frequency subbands of F4 and T3 channels included only delta and theta subbands. Furthermore, frequency increase rates of pre-ictal and ictal signals are investigated, and increasing PSDs of ripples and fast ripples are then calculated. A much higher-frequency ripple follows the low-frequency ripple in 75%, 75%, 65%, 85%, and 75% of the channels of the first, second, third, fourth, and fifth patients, respectively. For the pre-ictal data, a much higher frequency ripple is not seen, followed by a low-frequency ripple in 90%, 85%, 75%, 90%, and 90% of all channels of five patients, respectively. In addition, in this study, 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. Consequently, examining PSD and HFO signals ensures the detection of the differences between the data sets and detects the epileptic seizure patterns in all five patients.