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Disease recognition algorithm based on convolutional neural network-recurrent neural network-support vector machine mixed model

A convolutional neural network and cyclic neural network technology, which is applied in diagnostic recording/measurement, medical science, diagnosis, etc., can solve the problems of low classification accuracy and few classification categories, so as to improve the accuracy and overcome easy overfitting. Effect

Inactive Publication Date: 2019-09-06
CHONGQING UNIV
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  • Claims
  • Application Information

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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 recognition algorithm based on convolutional neural network-recurrent neural network-support vector machine mixed model
  • Disease recognition algorithm based on convolutional neural network-recurrent neural network-support vector machine mixed model
  • Disease recognition algorithm based on convolutional neural network-recurrent neural network-support vector machine mixed model

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Embodiment Construction

[0032] Below in conjunction with accompanying drawing, describe the implementation process of the inventive method in detail. It should be emphasized that the following descriptions are only exemplary and not intended to limit the scope of the present invention and its application.

[0033] In this patent, the portable EEG signal acquisition method is used to collect EEG data, and then the data analysis method is used to identify and classify the sample data. In this paper, the CNN-RNN-SVM algorithm is used. CNN and RNN are used to extract temporal and spatial features, and then the features are combined and then classified by SVM to obtain high accuracy, sensitivity and specificity results.

[0034] figure 2 It is an algorithm implementation process diagram, and the present invention comprises the following steps:

[0035] Step 1: Use the portable EEG signal acquisition method to collect EEG to obtain sample data, and perform data preprocessing on the data to obtain the in...

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Abstract

The invention designs a recognition algorithm based on a convolutional neural network-recurrent neural network-support vector machine mixed model for use in a noninvasive diagnostic system. A data setis acquired through pretreatment means, such as data noise pretreatment; the convolutional neural network and the recurrent neural network are used in the field for the first time as end-to-end feature extractors to perform feature extraction on samples; temporal and spatial features of data are extracted separately; the extracted feature data are classified with the support vector machine. The recognition algorithm has high accuracy, high specificity and high sensitivity and is widely applicable to the field of noninvasive detection.

Description

[0001] 【Technical field】 [0002] The invention patent is aimed at the field of non-invasive detection of diseases, especially the field of epilepsy screening. [0003] 【Background technique】 [0004] Although existing disease screening methods have high detection sensitivity and accuracy, they actually rely on expensive equipment and complicated operations, or cause some irreversible damage to the body. Therefore, there is a need for a low-cost, simple and effective non-invasive surgical method to screen for the disease. [0005] 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 an...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0476A61B5/04
CPCA61B5/7246A61B5/7267A61B5/316A61B5/369
Inventor 陆彬春符礼丹艾海男
Owner CHONGQING UNIV