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Epileptic seizure prediction method based on electroencephalogram signals

A technology of epileptic seizures and EEG signals, applied in the medical field, can solve problems such as increasing the burden on doctors and subjective judgment errors

Pending Publication Date: 2022-01-28
BEIJING UNIV OF TECH
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Problems solved by technology

However, the reading and analysis of EEG needs to be handled by experienced neurologists, which not only increases the burden on doctors, but also easily leads to subjective judgment errors

Method used

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  • Epileptic seizure prediction method based on electroencephalogram signals
  • Epileptic seizure prediction method based on electroencephalogram signals
  • Epileptic seizure prediction method based on electroencephalogram signals

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Embodiment Construction

[0053] The present invention will be further described below in conjunction with accompanying drawing:

[0054] Such as figure 1 Shown, the inventive method mainly comprises the following steps:

[0055] Step 1. Data preprocessing

[0056] The CHB-MIT epilepsy EEG data set was used for testing. First, the interictal and preictal EEG data were classified and stored according to the description of the data set, and the two types of data were segmented with a 30s EEG window.

[0057] Step 2. Feature extraction

[0058] After the segmented EEG data is subjected to empirical mode decomposition, the first three components are taken to calculate its entropy feature, and the feature vector is obtained. The eigenvectors are formed by figure 2 As shown, first divide the 30s EEG signal into 5 segments, then perform empirical mode decomposition on the 5 EEG segments and take the first three segments to extract its 6 entropy features, and finally get a length of 5*3* 6=90 feature seq...

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Abstract

The invention discloses an epileptic seizure prediction method based on electroencephalogram signals, which is used for predicting epileptic seizure by combining empirical mode decomposition and a convolutional neural network and helping doctors to diagnose. The method mainly comprises the following steps that electroencephalogram signals monitored for a long time are marked and segmented, empirical mode decomposition is conducted on segmented electroencephalogram data, entropy features are extracted, finally, the extracted features are learned through a convolutional neural network, and the electroencephalogram signals in the early stage of attack and in the interval of attack are classified. According to the method, a time-frequency domain and nonlinear feature extraction method is combined with a deep neural network classification method, so that the accuracy of epilepsy electroencephalogram signal prediction is effectively improved, a doctor patient can make full preparation before epileptic seizure comes, and epilepsy can be treated more effectively.

Description

technical field [0001] The invention relates to the construction of a model method in the medical field, and relates to a method for predicting epileptic seizures based on electroencephalogram signals. [0002] technical background [0003] Epilepsy is a chronic disease in which the sudden abnormal discharge of neurons in the brain leads to transient brain dysfunction. According to the statistics report of the World Health Organization, about 50 million patients are currently suffering from epilepsy, which has become one of the most common neurological diseases in the world. Epilepsy is characterized by recurrent and epileptic seizures. During epileptic seizures, patients will unconsciously develop symptoms such as general convulsions, loss of consciousness, and cognitive impairment, which have greatly affected the normal life of patients. Electroencephalography (EEG), which directly records the electrical activity of neurons in the brain through electrodes attached to the s...

Claims

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Application Information

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IPC IPC(8): A61B5/372
CPCA61B5/372A61B5/4094A61B5/7267
Inventor 闫健卓李晋楠许红霞
Owner BEIJING UNIV OF TECH
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