One-dimensional sequence dimension-raising clustering method and system

A clustering method and sequence technology, applied in neural learning methods, biological neural network models, sensors, etc., can solve the problems of low recognition accuracy, single processing and extraction features, etc., and achieve the effect of saving economic costs and easy feature extraction.

Pending Publication Date: 2021-01-26
山西三友和智慧信息技术股份有限公司
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Problems solved by technology

[0004] Aiming at the above-mentioned technical problems of single processing and extraction features of traditional ECG signals and low recognition accuracy, the present invention provides a one-dimensional sequence ascending dimension clustering with high classification accuracy, easy feature extraction, low cost and high degree of automation method and system

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  • One-dimensional sequence dimension-raising clustering method and system
  • One-dimensional sequence dimension-raising clustering method and system
  • One-dimensional sequence dimension-raising clustering method and system

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[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0029] A one-dimensional sequence ascending dimension clustering method, such as figure 1 , figure 2 , image 3 shown, including the following steps:

[0030] Step 1. ECG signal collection; import the one-dimensional ECG signal, divide it into ECG signal segments with a length of n, and store it as an NPY file for the model to read; the purpose of segmenting the ECG signal here is to unify Enter the size of the model data to facilitate model identification...

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Abstract

The invention belongs to the technical field of artificial intelligence image processing, and particularly relates to a one-dimensional sequence dimension-raising clustering method and system. The system comprises an electrocardiosignal collection module, a preprocessing module, a denoising module, a Gram transformation module, an unsupervised Kmeans clustering module, and an output result module.The electrocardiosignal collection module imports one-dimensional electrocarddiosignals, stores the one-dimensional electrocardiosignals in a database, and gives footnotes on the one-dimensional electrocardiosignals from X0 to Xn according to a time sequence of importing the one-dimensional electrocardiosignals into the database; the preprocessing module is used for scaling the time sequence X =x1, x2,..., xn of the one-dimensional electrocardiosignals; the denoising module is used for screening and denoising the one-dimensional electrocardiosignals; the Gram transformation is used for converting the one-dimensional electrocardiosignal into a two-dimensional electrocardiogram image; According to the method, recognition of electrocardiosignals is converted into an image classification problem from a time domain problem, the similarity in data is measured by conducting inner product on a time sequence, the similarity is converted into a Gram matrix, and the dimension increasing processfrom one dimension to two dimensions is completed.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence image processing, and in particular relates to a one-dimensional sequence ascending dimension clustering method and system. Background technique [0002] Electrocardiogram (ECG) is an intuitive record of heart activity. The characteristics of heart rate, S-T segment, P wave, QRS wave group shape and appearance position displayed by ECG can be used for sinus tachycardia and sinus arrhythmia. Diagnosis of arrhythmias such as arrhythmia, ventricular premature beats, and atrial fibrillation is one of the main basis for the identification of heart diseases. In the past, the identification of ECG signals mainly relied on the human judgment of doctors. With the continuous improvement of medical standards, the speed and accuracy of manual identification are no longer enough to meet medical needs. This technology can show the recessive characteristics of data to a great extent, improve the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/00A61B5/346A61B5/00G06N3/08
CPCA61B5/7264G06N3/08G06F2218/04G06F18/23213
Inventor 潘晓光李宇王小华刘剑超张娜焦璐璐
Owner 山西三友和智慧信息技术股份有限公司
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