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The electric stimulation of the vagus nerve is used to obtain therapeutic results in epilepsy, depression and Alzheimer diseases. The purpose of this paper is to show numerical model of stimulation, focusing on the mathematical approach to modeling a phenomenon of neural cells activation and its propagation in the nerve.

The paper presents a model based on the bidomain theory. It uses two continuous, averaged domains which depict the intra‐ and extra‐cellular domains and are connected with the membrane ionic currents. The numerical model uses 3D cylindrical model approximating the anatomical shape of the neck. The simulator is based on a time domain finite element method.

The presented approach allows to model the discrete behaviour of the membrane potential in the macroscopic, realistic model of the nerve. The validation of the parameters with the velocity of activation propagations suggests the strong disscussion on physical interpretation to the bidomain theory parameters. To obtain realistic results the parameters needed to be unrealistic.

The paper presents the combination of bidomain model of neural tissue with the time domain finite element method along with the atributes of bidomain model for realistic modeling of the process of propagtion of activation.

The purpose of this paper is to develop the new method of estimation of the short‐term largest Lyapunov exponent of electroencephalogram (EEG) waveforms for the detection and prediction of the epileptic seizure.

The paper proposed the modifications concerned with the way of selection of the segments of EEG waveforms taking part in estimation of Lyapunov exponent, as well as determination of the distances between two time series. The proposed method is based on Kolmogorov‐Smirnov test of similarity of two vectors. Through the application of this test more accurate and less parameterized approach to the estimation of the short‐term largest Lyapunov exponent of EEG waveforms has been obtained.

The results of performed experiments have shown that in most cases our modified method has outperformed the classical procedure, leading to more stable results, closer to the neurologist indications. The analysis of the data has proved that the change of the largest Lyapunov exponent provides a lot of information regarding the epileptic seizure. The minimum value of Lyapunov exponent indicates fairly well the seizure moment. The T_index applied for few different electrode sites can provide good advanced prediction of the incoming epileptic seizure.

After additional experiments this method may find practical application for supporting the medical diagnosis of the epilepsy.

The proposed modification of the estimation of the short‐term largest Lyapunov exponent of the EEG waveforms eliminates some arbitrarily chosen parameters tuned by the user and leads to more accurate estimate. Such estimation results are better suited for the characterization of the epileptic activity.