Aims: In irreversible electroporation process, the membrane of cancer cells is damaged irreversibly by electric pulses of high-intensity field, which in turn leads to cell death. Factors influencing the field distribution include voltage, pulse width, and electric conductivity of tissue. The present study was conducted with the aim of evaluating conductivity changes of liver tissue during irreversible electroporation and calculation of the electric field distribution.
Materials and Methods: In the present experimental study, using simulation, the relationship between pulse width and voltage intensity of each pulse was investigated in conductivity changes during irreversible electroporation, and the electric field distribution was calculated. In this simulation, in order to solve the equations, the software COMSOL 5 was used. Needle electrodes were used, and the liver tissue was considered as the target tissue. Eight pulses with the stimulated frequency of 1Hz, pulse width of 100µs and 2ms, and the intensity of the electric fields ranging from 1000 to 3000v/cm were used as electric pulses.
Findings: Conductivity of tissue increased during sending the electrical pulses. The conductivity changes in the tip of the electrodes were more than the area between the two rows of electrodes. As the intensity of the pulsed electric field increased, the tissue conductivity also increased. When the conductivity of the tissue was constant and variable, the maximum electric field intensity was obtained 3879 and 3448v/cm.
Conclusion: While electric pulse transmission, tissue conductivity increases. The electric field distribution depends on the conductivity at the desired point and by changing this conductivity due to the electroporation, the electric field distribution also changes and the maximum intensity of the electric field decreases.

Aims: Electroencephalogram (EEG) is an important clinical test for the diagnosis of many brain diseases. The aim of this study was the analysis of electroencephalogram data during rest in patients with brain tumor.
Materials and Methods: In the present analytic observational study, EEG data of 44 patients with brain tumor (tumoral group) and 31 healthy subjects (healthy group) during rest were used. After preprocessing, the linear temporal features, linear spectral features of different frequency bands, and non-linear features of fractal dimension and entropy were extracted. Then, the distinction between healthy and tumoral groups based on extracted features was investigated, using the Davis-Bouldin statistic method, linear discriminant analysis (LDA) and nonlinear K-Nearest Neighbor (KNN) classification.
Findings: There was no significant difference between the the fractal kutz dimension and the waveform length of the two healthy and tumoral groups. Among other features, the sample entropy with a significant reduction in the tumoral group made the most distinction between the two groups (0.69 for the healthy group and 0.53 for the tumoral group). The highest classification accuracy of the two groups was 84%, using the sample entropy and KNN classification.
Conclusion: EEG signals have the potential to distinct the patients with brain tumor and healthy subjects. Nonlinear entropy features with more adaptation to the nonlinear nature of the brain shows a higher accuracy in the representation of the tumoral group. The less entropy of the tumoral group indicates less complexity in the brain processing of this group than the healthy group.