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A GRNN-based identification method

An identity recognition and electrocardiographic signal technology, applied in the field of computer recognition, can solve problems such as large amount of data, and achieve the effects of low cost, strong usability and simple technology

Active Publication Date: 2019-01-08
JILIN UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to provide an efficient, low-calculation method that can realize automatic ECG signal identification, so as to solve the problem of huge data volume brought by the era of big data

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Embodiment Construction

[0026] The idea, specific structure and technical effects of the present invention will be clearly and completely described below in conjunction with the embodiments and accompanying drawings, so as to fully understand the purpose, scheme and effect of the present invention.

[0027]It should be noted that, unless otherwise specified, when a feature is called "fixed" or "connected" to another feature, it can be directly fixed and connected to another feature, or indirectly fixed and connected to another feature. on a feature. In addition, descriptions such as up, down, left, and right used in the present disclosure are only relative to the mutual positional relationship of the components of the present disclosure in the drawings. As used in this disclosure, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. Also, unless defined otherwise, all technical and scientific terms used herein have ...

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Abstract

The technical proposal of the invention comprises a GRNN-based identification method. The method is used for realizing the following steps: an electrocardiogram signal sample data set including a plurality of periodic beats of a plurality of users is acquired, and wavelet transform is adopted to remove noise in the electrocardiogram signal; the denoised signals are segmented to construct the morphological features of ECG signals, and the training set beat feature databases and the test set beat feature databases are constructed respectively; singular value decomposition is used to remove redundant features from ECG signals; dimension reduction of the heart beat feature data set is carried out by linear discriminant analysis; the generalized regression neural network classifier is trained and the identity information of the individual is output according to the principle of multi-beat voting. The method has the advantages that the redundant information in the electrocardiogram signal iseffectively removed, the accuracy rate of the subsequent identification is improved, and the speed of the identification is greatly improved due to the reduction of the heart beat characteristic dimension by the LDA and the use of the GRNN neural network as a classifier.

Description

technical field [0001] The invention relates to a GRNN-based identification method, which belongs to the field of computer identification. Background technique [0002] With the continuous development and breakthrough of information technology, the network has become an important part of our study, work and life. At the same time, personal identification has been widely used in various fields. However, due to the improvement of people's awareness of network security, traditional identification methods such as certificates, passwords, static passwords, etc. have been unable to meet people's needs because they are easy to be stolen, copied and cracked. With the continuous development of the field of artificial intelligence, biometric identification technology came into being. Identification methods such as face recognition, fingerprint recognition, iris recognition and electrocardiographic signal recognition have been proposed. Compared with other biological signals, ECG si...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06F21/32
CPCG06F21/32G06F2218/06G06F2218/10G06F2218/12G06F18/2132G06F18/2414G06F18/259G06F18/25
Inventor 司玉娟刘芳刘奇郎六琪
Owner JILIN UNIV
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