Application ofMultidimensional Characteristic Analysis of Electroencephalogram Signal in Epileptic Seizure Prediction
The nonlinear EEG signals of epilepsy patients face challenges such as difficulty in classifying and recog-nizing patterns.In view of this,this study constructs a joint EEG signal classification model based on convolutional neural networks combined with various intelligent optimization algorithms,and verifies its convergence and classifi-cation performance through experiments.The model can accurately test the corresponding changes in EEG signals under different frequencies of brain stimulation.And the convergence efficiency of the joint algorithm was tested by selecting a dataset.The convergence speed of the joint algorithm from the 10th iteration was significantly better than the other algorithms,and it still had a significant advantage in the 200th generation.The classification efficien-cy of the joint algorithm is about 10％higher than that of traditional extreme learning machines.Overall,this mod-el has played a role in collecting,analyzing,and classifying the EEG signals of epilepsy patients in practical diagnos-tic scenarios,and has certain practicality and reference value for the diagnosis and prediction of seizures.
EpilepsyEeg signalConvolutional neural networkIntelligent optimization algorithmClassification model
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