A grnn-based identification method

A technology of identification and electrocardiogram, applied in the field of computer identification, which can solve problems such as large amount of data

Active Publication Date: 2021-12-07
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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  • A grnn-based identification method
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  • A grnn-based identification method

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Embodiment Construction

[0025] 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.

[0026] 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 solution of the present invention includes a GRNN-based identity recognition method, which is used to realize: obtain an ECG signal sample data set including multiple cycles of heartbeat data of multiple users, and use wavelet transform to remove the ECG signal noise; the denoised signal is segmented to construct the morphological features of the ECG signal, and the heartbeat feature database of the training set and the heartbeat feature database of the test set are respectively constructed; the singular value decomposition method is used to remove the redundancy in the ECG signal. Additional features; using linear discriminant analysis to reduce the dimensionality of the heart beat feature data set; training a generalized regression neural network classifier, and outputting the identity information of the individual according to the principle of multi-heart beat voting. The beneficial effects of the present invention are: the redundant information in the electrocardiogram signal is effectively removed, and the accuracy rate of subsequent identification is improved, while the reduction of the feature dimension of the heartbeat by LDA and the use of the GRNN neural network as a classifier greatly improve the identification speed.

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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Patent Type & Authority Patents(China)
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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