EEG classification method adaptive to different sampling frequencies

Abstract

The invention relates to an EEG (electroencephalogram) classification method adaptive to different sampling frequencies, and relates to a signal classification method. The EEG classification method adaptive to different sampling frequencies includes the steps: 1) construction of a CNN-E classification model based on a convolutional neural network; and 2) training and testing method for sample dataof different lengths. The model can be used to learn and classify EEG signals at different sampling frequencies, and can be adaptive to signals of different lengths. The model and a traditional classification method based on feature extraction can analyze the possible problems in the classification of EEG signals at different sampling frequencies. The network model CNN-E can be adaptive to various lengths of data by means of autonomous learning of the sample data, and by means of a simple and effective completion method. The experimental results show that the network model CNN-E can achieve well classification effect and preferable universality for classification of EEG signals at the same sampling frequency, classification of EEG signals at different sampling frequencies, and classification of EEG signals with different sample lengths.

Description

technical field [0001] The invention relates to a signal classification method, in particular to an EEG classification method adaptive to different sampling frequencies. Background technique [0002] Epilepsy is characterized by repeated epileptic seizures caused by abnormal discharge of brain neurons, and its repeated seizures often bring physical and psychological harm to patients. There are now about 50 million epilepsy patients in the world, and epilepsy has become one of the most common neurological diseases that endanger human health worldwide [1] . Brain waves are formed by the sum of the synchronous post-synaptic potentials of a large number of neurons when the brain is active. It can record the changes in electrical waves during brain activity and reflect the electrophysiological activities of the cerebral cortex or scalp surface of brain nerve cells. [2] . Brain wave analysis has become an effective and important method for epilepsy research. [0003] From the ...

AI Technical Summary

Problems solved by technology

In practical applications, it is often possible to face more complex data, manual feature design is often difficult, data processing is cumbersome and the results are difficult to control

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