Epilepsy seizure prediction method based on semi-supervised deep generative adversarial network

Abstract

The invention discloses an epilepsy seizure prediction method based on a semi-supervised deep generative adversarial network. The method comprises the following steps: carrying out segmentation windowing processing on each EEG(electroencephalography) data of N original EEG samples through a sliding window in specified length, and during segmentation, carrying out superposition expansion processingon adjacent sub-windows to obtain S sections of sub-signals; carrying out short-time Fourier transform on the S sections of sub-signals to obtain spectrum signals, and converting the obtained spectrum data into an image for expression; inputting the processed picture information to a discriminator in the semi-supervised deep generative adversarial network for training and classification accordingto labeled training samples, training sample labels and unlabeled training samples; and inputting Z-dimensional noise data into a generator, and generating a pseudo-data image similar to distributionof the unlabeled data through the generator. The method gradually releases dependence of epilepsy seizure prediction on the labeled data through a semi-supervised learning mode, and obtains better classification accuracy.

Description

technical field [0001] The invention relates to the field of EEG signal processing technology and deep learning, in particular to a method for classifying EEG signals in different stages of epileptic seizures. Background technique [0002] Epilepsy is a common brain disease characterized by recurrent seizures. There are about 65 million people in the world suffering from epilepsy. In my country, there are currently 6 million to 9 million epilepsy patients and the rate is increasing at a rate of about 400,000 per year. Epilepsy has a great impact on the life and spirit of patients and their families, and even endangers their lives. Epilepsy is a common emergency in internal medicine. If it is not treated in time, high fever, circulatory failure, electrolyte imbalance or neuronal excitotoxicity damage can lead to permanent brain damage, with high disability and mortality rates. Status epilepticus can occur in any type of epilepsy, of which generalized tonic-clonic seizures ar...

AI Technical Summary

Problems solved by technology

This makes manual screening of features particularly difficult

Therefore, accurate prediction of seizures remains a challenge

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