Computer mode recognition method for brain electrical signals of epilepsy patients

Abstract

The invention discloses a computer mode recognition method for bran electrical signals of epilepsy patients, and relates to the technical field of brain science and epileptic seizure clinical data recognition. The method includes the steps that firstly, a random forest recognition model is constructed and then trained so that the optimal random forest recognition mode can be generated; mode recognition testing of the brain electrical signals of the epilepsy patients with different degrees of disease conditions is conducted on the optimized random forest model on a test set. Through the computer mode recognition method, the function that through a computer, the brain electrical signals of the epilepsy patients are automatically recognized is realized, and technical supports are provided when medical workers consumes time and labor for diagnosis. A grid search optimization method is introduced, a variable step size mode is adopted for repeatedly filtering parameters to accelerate optimalcombining of search parameters, the operation efficiency of the random forest model is improved, the trained random forest recognition model realizes the optimal effect, and the accuracy of mode recognition conducted on three kinds of epilepsy disease conditions can reach 96% or above.

Description

technical field [0001] The invention relates to the technical fields of brain science and epileptic seizure clinical data recognition, in particular to a computer pattern recognition method for EEG signals of epileptic patients. Background technique [0002] The seizures of epilepsy are clinical manifestations of paroxysmal abnormal hypersynchronous electrical activity of neurons in the brain, which are characterized by repetition, suddenness and temporaryity. As an important tool for studying epilepsy, EEG signals can reflect seizure information in real time that cannot be provided by other physiological methods. At present, in the analysis and research of EEG signals of epileptic patients, machine learning is a powerful tool for the identification of EEG signals in epilepsy. However, most machine learning methods to identify EEG signals have a relatively complicated calculation process, and the accuracy of the recognition method cannot be guaranteed. and effectiveness. T...

AI Technical Summary

Problems solved by technology

These methods have different recognition accuracy on data sets of different brain diseases, and there is still inapplicability to the computer EEG pattern recognition method itself

In addition, the parameter selection of the identification method relies on the manual parameter adjustment by researchers with rich experience, and it cannot fully guarantee that the generated parameters are the optimal parameters of the model of the identification method.

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