Abstract
Medical and biological electroencephalogram (EEG) signals are widely used for diagnosis, further medical support and treatment of such disease as epilepsy. A method for detecting of epileptiform activity presence in patients has been developed based on analysis of EEG signals taken during the 1st, 2nd and 3rd sleep phases. To implement this task, an approach was designed with use of genetic algorithm and artificial neural network (ANN), which can be classified according to the following criteria: the network is analog; self-organizing; a direct distribution network, static. The input neurons count in the neural network is equal to the channels count in the EEG recording, and in this study it is equal to 21, 15 neurons in the hidden layer, 1 output neuron. The input layer accepts data in the form of numbers representing the calculated characteristics for each channel: the largest Lyapunov exponent calculated by the Rosenstein, Wolf, Sano-Sawada methods, for men and women with epilepsy and from the control group. Test sample: 33 patients, of which 6 were healthy and 27 patients with epilepsy with different diagnoses. Some of the combinations made it possible to obtain 100% accuracy in determining the presence or absence of disease in patients.
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Yakovleva, T.V., Dobriyan, V.V., Yaroshenko, T.Y., Krysko-jr, V.A. (2022). Mathematical Modeling and Diagnostics Using Neural Networks and a Genetic Algorithm for Epilepsy Patients. In: Badriev, I.B., Banderov, V., Lapin, S.A. (eds) Mesh Methods for Boundary-Value Problems and Applications. Lecture Notes in Computational Science and Engineering, vol 141. Springer, Cham. https://doi.org/10.1007/978-3-030-87809-2_42
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