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Epilepsy EEG Signal Recognition Method Based on Optimal Kernel Time-Frequency Distribution Visualization

A time-frequency distribution, EEG signal technology, applied in diagnostic signal processing, medical science, diagnosis and other directions, can solve problems such as strong background noise, EEG signal analysis and processing difficulty, etc.

Active Publication Date: 2019-06-18
钧晟(天津)科技发展有限公司
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Problems solved by technology

However, since the EEG signal is a non-stationary random signal without ergodicity, and its background noise is very strong, the analysis and processing of the EEG signal has always been a very attractive but quite difficult research topic.

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  • Epilepsy EEG Signal Recognition Method Based on Optimal Kernel Time-Frequency Distribution Visualization
  • Epilepsy EEG Signal Recognition Method Based on Optimal Kernel Time-Frequency Distribution Visualization
  • Epilepsy EEG Signal Recognition Method Based on Optimal Kernel Time-Frequency Distribution Visualization

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[0102] Five types of EEG signals were collected from 10 people, among which, type A: the EEG data of five healthy people with their eyes open, type B: the EEG data of the same five healthy people with their eyes closed, type C: five The EEG signals of the non-epileptic focal area of ​​a patient with epilepsy without seizures, category D: the EEG signals of the same five patients with epilepsy in the epileptic focus area without seizures, category E: EEG signals of the epileptic focus during seizures in the same five patients with epilepsy. The electrode placement method of each person was placed according to the 10-20 international standard, the sampling frequency was 173.61 Hz, and the sampling time was 23.6 seconds. After preprocessing the collected raw EEG data, the denoised EEG data can be obtained. The following two examples are used to verify the effectiveness of this method: (1) the distinction between A, B and E data, by distinguishing normal EEG signals from epilepti...

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Abstract

The invention relates to an epilepsy electroencephalogram signal identification method based on an optimal kernel time-frequency distribution visibility graph, which comprises: acquiring original electroencephalogram data, and calculating adaptive optimal kernel time-frequency distribution of a preprocessed electroencephalogram signal; carrying out classification of epilepsy states; for each obtained time energy sequence, carrying out visualization analysis, obtaining an energy time sequence visibility graph complex network, extracting to obtain feature indexes of the energy time sequence visibility graph complex network, extracting adaptive optimal kernel time-frequency distribution indexes, and classifying the epilepsy electroencephalogram (EEG) signal by combining a support vector machine. According to the epilepsy electroencephalogram signal identification method based on the optimal kernel time-frequency distribution visibility graph, which is disclosed by the invention, the energy time sequence visibility graph complex network is constructed by combining an adaptive optimal kernel time-frequency distribution principle and a visibility graph idea, the indexes of the complex network are extracted, and identification on the epilepsy electroencephalogram signal is implemented.

Description

technical field [0001] The invention relates to a recognition method of epilepsy brain electric signals. In particular, it relates to a method for identifying epileptic EEG signals based on a visual map of optimal nuclear time-frequency distribution. Background technique [0002] Epilepsy is a chronic disease in which the sudden abnormal discharge of brain neurons leads to transient brain dysfunction. Clinically, it manifests as paroxysmal motor, sensory, autonomic, conscious, and mental disturbances. As a chronic disease, epilepsy does not have much impact on patients in the short term, but frequent and long-term seizures can seriously affect the patient's body, mind and intelligence. EEG signals are the overall reflection of the electrophysiological activities of brain nerve cells on the surface of the cerebral cortex or scalp. EEG signals contain a large amount of physiological and disease information. Analyzing the EEG signals of epilepsy patients and healthy people c...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/0476
CPCA61B5/4094A61B5/72A61B5/7203A61B5/725A61B5/7264A61B5/369
Inventor 高忠科蔡清张珊珊杨宇轩
Owner 钧晟(天津)科技发展有限公司
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