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EEG signal classification model based on genetic algorithm and random forest

A genetic algorithm and random forest technology, applied in the field of EEG signal classification model based on genetic algorithm and random forest, can solve the problem of low accuracy

Pending Publication Date: 2018-10-02
XIAMEN UNIV
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Problems solved by technology

In addition, the accuracy of the results obtained by classifying EEG signals based only on the features obtained by a single feature extraction method is generally not high

Method used

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  • EEG signal classification model based on genetic algorithm and random forest
  • EEG signal classification model based on genetic algorithm and random forest
  • EEG signal classification model based on genetic algorithm and random forest

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Embodiment Construction

[0043] The following embodiments will further illustrate the present invention in conjunction with the accompanying drawings.

[0044] The present invention provides a kind of EEG signal classification model based on genetic algorithm and random forest, can obtain higher classification accuracy, such as figure 1 As shown, the embodiment of the present invention includes the following steps:

[0045] 1) Feature extraction:

[0046] First, the experimental data is divided into training data and test data, and three methods of time domain, empirical mode decomposition and frequency domain feature search based on genetic algorithm are used to extract features from the training data set. Extracting features directly from the time domain is the earliest developed method, because it is intuitive and has a relatively clear physical meaning. It has been widely used in the field of EEG signal applications. The root mean square, waveform length, and absolute value integration are select...

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Abstract

The invention provides an EEG signal classification model based on a genetic algorithm and a random forest, and relates to classification of EEG signals. The model comprises steps of 1) feature extraction; 2) feature optimization selection; and 3) classifier model training and prediction. According to the invention, feature learning and classification are performed on a public EEG data set. An experiment result shows that feature distribution after the optimization selection is quite good; compared with the existing researches of the same type, the classification accurate of the provided method is higher than that of the current classification method; by carrying out crossed verification on the data set, validity and stability are achieved in brain wave classification; and the result showsthat the model has quite high application value and wide prospects in clinic application in future.

Description

technical field [0001] The invention relates to EEG signal classification, in particular to an EEG signal classification model based on genetic algorithm and random forest. Background technique [0002] EEG signals contain a large amount of physiological and pathological information, and play an important role in the research of clinical medicine and brain science. Epilepsy is the result of sudden excessive repetitive discharges of central neuronal populations [1] . The treatment of epilepsy patients requires more accurate positioning of the epileptogenic region of the patient. Accurate analysis of the patient's EEG signal helps to determine the epileptogenic region, which will make the epileptogenic region resection more accurate and improve the efficacy of epilepsy surgery. However, at present, the detection and analysis of abnormal EEG activity is mainly done by doctors through visual inspection. This detection is time-consuming, inefficient, and prone to errors. The...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/12
CPCG06N3/126G06F2218/10G06F2218/12G06F18/241G06F18/214
Inventor 张仲楠罗威臻
Owner XIAMEN UNIV
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