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Disease diagnosis system based on GRU circulating neural network

A technology of cyclic neural network and diagnostic system, which is applied in the field of data analysis of non-invasive detection system, can solve the problems of few classification categories and low classification accuracy, and achieve the effect of improving the diagnosis effect

Inactive Publication Date: 2019-10-01
CHONGQING UNIV
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Problems solved by technology

At present, scholars at home and abroad have made some research and analysis on the diagnosis of epilepsy, but there are still problems of few classification categories and low classification accuracy.

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  • Disease diagnosis system based on GRU circulating neural network
  • Disease diagnosis system based on GRU circulating neural network
  • Disease diagnosis system based on GRU circulating neural network

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Embodiment Construction

[0015] In the method of data analysis in the GRU-based disease screening described in the present invention, step S1. Use the portable EEG signal acquisition terminal to collect EEG data and save it.

[0016] Step S2. In the data preprocessing, the filtering method is used to denoise, the wavelet is used for filtering, and the EEG signal is decomposed and reconstructed to obtain the time domain information of the EEG signal.

[0017] Step S3. After the data preprocessing is completed, use the Gated Recurrent Unit (GRU) method to train the data and use the "memory function" of the GRU to use the "memory unit" at the previous moment and the input at the previous moment together as Input to the grid, and select, retain and input the passed information by resetting the gate and updating the gate, so that all information of the data can be used;

[0018] Step S4. In the process of GRU training the data set, the parameters are adjusted through the feedback signal, and the number of ...

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Abstract

The invention relates to an epilepsy non-invasive detection data analysis method based on a GRU circulating neural network. The electroencephalogram data of a volunteer is analyzed, and whether or notthe volunteer suffers from epilepsy is judged according to electroencephalogram information. The method solves the problems of high misdiagnosis rate, high missed diagnosis rate and the like of epilepsy diagnosis due to the fact that electroencephalogram data and detection data are high in dimension, strong in time sequence performance and the like. The collected electroencephalogram data is processed according to the data analysis method based on the GRU circulating neural network, and the method mainly comprises the following steps of S1, utilizing a portable electroencephalogram signal collecting side to collect and save electroencephalogram signals of the volunteer; S2, preprocessing the collected electroencephalogram data such as filtering; S3, utilizing GRU to train a preprocessed training set; S4, adjusting parameters through feedback signals in a training process; S5, obtaining a classification model which is suitable for classifying the electroencephalogram signals and judging whether or not the volunteer suffers from epilepsy.

Description

【Technical field】 [0001] The invention relates to the technical field of data analysis of a noninvasive detection system, in particular to a GRU cycle neural network data processing method for epilepsy diagnosis, which is an important part of epilepsy diagnosis by using electroencephalogram signals. 【Background technique】 [0002] EEG contains a lot of physiological and pathological information, can be measured directly on the human body, is suitable for clinical application, can provide diagnostic basis for some brain diseases, and even become an effective treatment for some brain diseases. In recent years, people pay more and more attention to the study of cognitive function. Effective analysis and evaluation of cognitive function is of great significance to the detection and treatment of cognitive impairment diseases. Epilepsy is a chronic disease in which the abnormal discharge of neurons in the brain leads to partial or complete brain dysfunction. EEG contains a wealth ...

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Application Information

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IPC IPC(8): A61B5/0476
CPCA61B5/4094A61B5/7267A61B5/316A61B5/369
Inventor 陆彬春符礼丹艾海男
Owner CHONGQING UNIV
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