Method for identifying and classifying epileptiform discharges, system, device thereof and medium

A classification method and epilepsy technology, applied in medical science, diagnostic signal processing, sensors, etc., can solve problems such as confusion, noise and chaos, and achieve high recognition and classification accuracy and high accuracy

Active Publication Date: 2019-10-18
BEIJING NORMAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] 2. Sharp wave
A further difficulty is that, for any noise, λ 1 may always be greater than 0, which can easily lead to confusing noise and real chaos

Method used

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  • Method for identifying and classifying epileptiform discharges, system, device thereof and medium
  • Method for identifying and classifying epileptiform discharges, system, device thereof and medium
  • Method for identifying and classifying epileptiform discharges, system, device thereof and medium

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Experimental program
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Embodiment 1

[0091] Embodiment one, as figure 2 As shown, a method for identifying and classifying epileptiform discharges, comprising the following steps:

[0092] S1: Obtain multiple original multi-lead EEG data;

[0093] S2: performing preprocessing on each of the original multi-lead EEG data respectively, to obtain a plurality of lead models corresponding to each of the original multi-lead EEG data;

[0094] S3: Using the power spectral density method and the scale-dependent Lyapunov exponent method to perform feature extraction on each of the lead models, and obtain a plurality of feature index sets corresponding to each of the lead models;

[0095] S4: Construct a target random forest classifier according to all the feature index sets;

[0096] S5: Identify and classify the EEG signal to be detected according to the target random forest classifier, and obtain a detection result.

[0097] In this embodiment, the original multi-lead EEG time will be obtained for preprocessing, so a...

Embodiment 2

[0143] Embodiment two, such as Figure 9 As shown, a recognition and classification system for epileptiform discharges, including a data acquisition module, a data processing module, a feature extraction module, a classifier building module and a recognition and classification module;

[0144] The data acquisition module is used to acquire a plurality of original multi-lead EEG data;

[0145] The data processing module is used to preprocess each of the original multi-lead EEG data, and obtain a plurality of lead models corresponding to each of the original multi-lead EEG data;

[0146] The feature extraction module is used to perform feature extraction on each of the lead models by using the power spectral density and the scale-dependent Lyapunov exponent method to obtain a plurality of one-to-one corresponding to each of the lead models. A collection of feature indicators;

[0147] The classifier construction module is used to construct a target random forest classifier acc...

Embodiment 3

[0150] Embodiment 3. Based on Embodiment 1 and Embodiment 2, this embodiment also discloses a device for identifying and classifying epileptic discharges, including a processor, a memory, and a device stored in the memory and operable on the processor. A computer program on the computer program, when the computer program runs, it realizes as image 3 The specific steps from S1 to S5 are shown.

[0151] The recognition and classification of the epileptiform discharge of the present invention can be realized by the computer program stored in the memory and run on the processor, which can make up for the deficiency of the traditional nonlinear signal processing method in the digital EEG signal analysis, and filter out the abnormal discharge. High-frequency artifacts reduce the influence of artifacts on EEG signals, make up for the confusion of chaotic noise and real chaos by traditional Lyapunov exponents, and describe the dynamics of EEG signals; using a combination of linear an...

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Abstract

The invention relates to a method for identifying and classifying epileptiform discharges, a ystem, a device thereof and a medium. The method comprises the following steps: acquiring a plurality of original multi-lead EEG data; respectively pre-processing each original multi-lead EEG data to obtain a plurality of lead models corresponding to each original multi-lead EEG data; employing a power spectral density method and a scale-dependent Lyapunov exponential method to extract features of each lead model respectively, and obtaining a plurality of feature index sets corresponding to each of thelead models one by one; constructing a target random forest classifier according to all the feature index sets; and perofmirng identification and classification on the EEG signals to-be-detected according to the target random forest classifier, and obtaining the test results. The method can make up for the deficiency of a traditional nonlinear signal processing method in the digitized EEG signalanalysis, realizes the classification of the normal human brain electrical signal and the epileptiform discharges, and has high recognition and classification accuracy.

Description

technical field [0001] The present invention relates to the technical field of digital EEG signal processing and analysis, in particular to a method, system, device and medium for identifying and classifying epileptiform discharges. Background technique [0002] The brain, as the most important organ of the human body, has a very complex structure and function. With the continuous development of neuroelectrophysiological technology, the research on human brain function has developed from the comprehensive EEG activity recorded by scalp electrodes to the recording by patch clamp. Cell membrane single channel potential, combined with molecular biology techniques, further elucidates the mystery of human brain activity. As far as clinical application is concerned, electroencephalogram (Electroencephalogram, EEG) is the spontaneous and rhythmic electrical activity of brain cell groups recorded by electrodes. An important method, especially in solving the qualitative and localiza...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0476
CPCA61B5/72A61B5/7203A61B5/7225A61B5/7267A61B5/369
Inventor 李琼张子闻高剑波黄淇吴原
Owner BEIJING NORMAL UNIVERSITY
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