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Electroencephalogram detection method and device by utilizing fluctuation index and training for promotion

A technology of fluctuation index and training method, applied in the field of EEG detection, can solve problems such as training failure, slow training speed, and low calculation efficiency

Inactive Publication Date: 2013-09-04
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The neural network must perform multiple repeated learning, the training speed is slow, and the calculation efficiency is low
At the same time, since the BP algorithm is an optimization algorithm for local search, if it is used to solve the global extremum of complex nonlinear functions, it is very likely to fall into the local extremum and cause the training to fail.

Method used

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  • Electroencephalogram detection method and device by utilizing fluctuation index and training for promotion
  • Electroencephalogram detection method and device by utilizing fluctuation index and training for promotion
  • Electroencephalogram detection method and device by utilizing fluctuation index and training for promotion

Examples

Experimental program
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Effect test

Embodiment 1

[0057] like figure 1 As shown, an EEG detection method using volatility index and boost training, the steps are as follows:

[0058] 1) Use Neurofile NT EEG amplifier and 16-bit A / D conversion data acquisition card to collect EEG signals, the sampling frequency is 256Hz, and store the collected EEG signals in the computer through A / D conversion.

[0059] 2) The computer filters and denoises the EEG signal, and the method steps are as follows:

[0060] Collect a piece of EEG signal with a length of LEN=1024, use Daubechies-4 wavelet to perform S-level wavelet decomposition, preferably S=5; then perform signal reconstruction on the decomposed EEG signal, and extract the 3-30Hz frequency band of the reconstructed signal , that is, the reconstructed signal a of the 3rd, 4th, and 5th layers j，n , a j，n Represents the jth channel signal x of the EEG signal whose length is LEN j The wavelet reconstruction signal of the nth layer of , where j=1, 2, . . . , C, n=3, 4, 5; C is the n...

Embodiment 2

[0096] A device utilizing the method described in embodiment 1 for EEG detection, comprising an EEG amplifier connected with a circuit, a data acquisition card and a computer, the computer is built-in an EEG that utilizes fluctuation index and lifting training method to detect EEG The detection module uses the EEG amplifier and data acquisition card to collect the EEG signal and transmits it to the computer, and uses the fluctuation index and the improvement training method to detect the EEG. The EEG detection module performs filtering and denoising processing on the EEG signal; The fluctuation index of each EEG signal is used as a feature vector; the feature vector is sent to the classifier obtained by the improved training method, and the output probability value is obtained; the output probability value is compared with the preset threshold value, and the EEG detection result is obtained. mark.

[0097] Using the invention to detect the EEG of 21 cases of epilepsy patients,...

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Abstract

The invention relates to an electroencephalogram detection method and an electroencephalogram detection device by utilizing a fluctuation index and training for promotion. Electroencephalogram data which is acquired and processed is subjected to feature extraction by using the fluctuation index with a good feature effect, and the extracted feature vector is input into a classifier obtained through a method of training for promotion, so that marks of abnormal electroencephalogram signals are obtained. Therefore, the workload of clinicians for judging large-scale electroencephalogram data is relieved, and the timeliness of abnormal electroencephalogram detection is improved.

Description

technical field [0001] The invention discloses an electroencephalogram detection method and device utilizing fluctuation index and boost training, and belongs to the technical field of electroencephalogram detection. technical background [0002] Epilepsy is a brain disorder characterized by intermittent central nervous system dysfunction caused by repeated sudden and excessive discharge of neurons in the brain. So far, epilepsy detection is mainly done by medical workers relying on experience to visually inspect the electroencephalogram (EEG) to check whether the EEG contains characteristic waves such as epileptiform discharges. sentence. Therefore, in epilepsy detection, the accuracy of EEG detection by automatic detection system has a more important position, and it can greatly improve the detection efficiency of EEG. [0003] Since the 1960s, automatic epilepsy detection technology has received extensive attention, and many scholars in this field have proposed a variet...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/0476
Inventor 周卫东陈爽爽
Owner SHANDONG UNIV