Epileptic feature extraction and automatic identification method based on electroencephalogram signal

Abstract

The invention brings forward an epileptic feature extraction and automatic identification method based on an electroencephalogram signal. The method comprises following steps: firstly, carrying out wavelet transformation to the electroencephalogram signal to obtain a time frequency image and segmenting the time frequency image into time frequency sub-images respectively having five frequencies including delta, theta, alpha, beta and gamma in the sequence from low to high frequencies; secondary, applying a Gaussian mixture model to estimate the probability distribution of the energy density of the time frequency image and utilizing parameters (mean value, variance, weight number) corresponding to the Gaussian mixture model as features of the electroencephalogram signal; thirdly, applying a feature weighting relief F and a support vector machine-recursive feature elimination to select above features in order to obtain the feature representing the difference between a normal electroencephalogram signal and an epileptic electroencephalogram signal to the greatest extent; lastly, verifying effectiveness for automatic identification of epilepsy represented by the method of the invention in the modes of pattern classification and machine learning, concretely speaking, accuracy of identification and generalization performance of the model. Compared with a conventional method, the epileptic feature extraction and automatic identification method based on the electroencephalogram signal has following beneficial effects: features obtained by extraction and identification have the high accuracy for identification of epileptic electroencephalogram; fine generalization performance of model is obtained; and important significance to auxiliary respects such as clinical diagnosis and automatic identification epileptic brain diseases is gained.

Description

technical field [0001] The invention relates to time-frequency analysis, pattern classification and machine learning of non-stationary signals, and belongs to the technical field of signal processing and pattern recognition. Background technique [0002] Epilepsy is a common frequently-occurring disease, which greatly endangers people's health and can cause death in severe cases. Electroencephalogram (EEG) signals are a necessary basis for diagnosing epilepsy. At present, epilepsy diagnosis is mainly completed by doctors' visual inspection of EEG. There are relatively large subjective factors in visual inspection, and different doctors or the same doctor may have inconsistent judgments on the same waveform at different times. Therefore, the epilepsy feature extraction and automatic recognition technology of EEG signals will greatly reduce the burden on doctors and improve the efficiency of EEG diagnosis. [0003] The analysis and processing of EEG signals has always been a...

AI Technical Summary

Problems solved by technology

The above-mentioned t-test method, variance analysis method, Pearson correlation method, and ReliefF method are all filter-type feature selection algorithms. Feature selection has nothing to do with specific classification algorithms. It has the characteristics of simple calculation and fast speed, but it lacks consideration of features Dependency and Redundancy

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