EEG classification method adaptive to different sampling frequencies

A technology of sampling frequency and classification method, applied in applications, medical science, sensors, etc., can solve problems such as difficulty in manual feature design, difficult control of results, and cumbersome data processing.

Pending Publication Date: 2018-12-18
XIAMEN UNIV
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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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  • EEG classification method adaptive to different sampling frequencies
  • EEG classification method adaptive to different sampling frequencies
  • EEG classification method adaptive to different sampling frequencies

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Embodiment Construction

[0049] The following embodiments will further illustrate the present invention in conjunction with the accompanying drawings

[0050] Embodiments of the present invention include the following steps:

[0051] 1) Convolutional neural network is a feed-forward neural network that improves the classification ability of patterns through posterior probability. The network mainly includes a convolutional layer, a pooling layer, a fully connected layer and a softmax layer. The convolutional layer performs convolution calculation on the input signal data through different convolution kernels to obtain feature maps (the number of convolution kernels is equal to the number of feature maps. ). The pooling layer is the process of downsampling the feature map obtained by the convolution operation of the previous layer. The network often increases the depth of the network by continuously iterating the convolutional layer and pooling layer, while the fully connected layer fully connects th...

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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 ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0476
CPCA61B5/4094A61B5/7264A61B5/369
Inventor 张仲楠温廷羲
Owner XIAMEN UNIV
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