Epilepsy signal classification method based on EWT and improved CSP

A technology of signal classification and epilepsy, applied in the field of pattern recognition, can solve the problems of lack of information of spatial characteristics, lack of results, and influence

Pending Publication Date: 2020-11-13
HANGZHOU DIANZI UNIV
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Problems solved by technology

These methods mainly extract the characteristics of the signal in the time-frequency domain, but the information of the epileptic EEG signal is reflected in the three feature domains of the time domain,

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  • Epilepsy signal classification method based on EWT and improved CSP
  • Epilepsy signal classification method based on EWT and improved CSP
  • Epilepsy signal classification method based on EWT and improved CSP

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Embodiment Construction

[0078] Embodiments of the present invention are described in detail below in conjunction with accompanying drawings: figure 1 Shown, the present invention comprises the following steps:

[0079] Step 1. Data preprocessing.

[0080] The epilepsy EEG data used in this example are selected from the CHB-MIT public database, and the first patient is used as the experimental data. The data channel is 23, and the data is divided into normal data and onset data. The 60 minutes before the onset of onset time is selected as normal data, and then the normal data is divided into inter-onset period and pre-onset period. Such as figure 2 As shown, 30 minutes before the onset of onset time was selected as the pre-ictal period, and 30 to 60 minutes before the onset of onset time was selected as the interictal period. The normal data before the onset of the patient's first three episodes were taken as the training set, and the normal data before the last two episodes were used as the test ...

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Abstract

The invention provides an epilepsy signal classification method based on EWT and improved CSP. The method comprises the following steps: preprocessing the signal, preferentially selecting a signal channel by utilizing correlation analysis, and extracting the characteristics of three characteristic domains, namely a time domain, a frequency domain and a space domain, of the epileptic electroencephalogram signal in an attack interval and an attack early stage; and finally, inputting the combined feature matrix of the three feature domains into a classifier based on a support vector machine to realize effective identification of the epileptic electroencephalogram signal attack interval and the epileptic electroencephalogram signal attack earlier stage. The time domain features comprise a meansquare value root, an absolute average value and a zero crossing point number. According to the frequency domain feature extraction method, empirical wavelet transform is used for carrying out multi-mode decomposition, a Welch power spectrum is used for carrying out single-mode selection, and then a Hilbert transform method is used for extracting the instantaneous amplitude and instantaneous frequency of a signal for the single mode of each channel; and an improved CSP algorithm is used for extracting airspace features. According to the method, the limitation of an EMD method is overcome through empirical wavelet transform, and in addition, the improved CSP algorithm has high recognition rate and calculation efficiency.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a method for pattern recognition of electroencephalogram signals, in particular to a classification method for epilepsy electroencephalogram signals. Background technique [0002] Epilepsy is one of the most common neurological diseases. It is a disorder of brain function caused by sudden abnormal discharge of neurons in the brain. The seizures of epilepsy are generally very sudden, and the duration of each seizure is different, so the seizures are highly uncertain. When an epileptic seizure occurs, the patient will generally experience loss of consciousness, convulsions all over the body, foaming at the mouth, etc., and even threaten the patient's life. At present, according to the World Health Organization (WHO) survey, there are about 50 million people in the world suffering from epilepsy, of which 9 million are in China. Long-term and repeated epileptic seizures not only ma...

Claims

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V40/15G06F2218/12
Inventor 孟明刘欣
Owner HANGZHOU DIANZI UNIV
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