12-lead electrocardiosignal multi-label classification method based on neural network

A neural network and classification method technology, applied in the field of computer image processing, can solve problems such as missing information and deviation of analysis results, and achieve the effect of improving accuracy

Inactive Publication Date: 2020-04-17
TAIYUAN UNIV OF TECH
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Problems solved by technology

However, such an operation will miss the rich information in the 12-lead ECG signal data, making the analysis results deviate.

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  • 12-lead electrocardiosignal multi-label classification method based on neural network
  • 12-lead electrocardiosignal multi-label classification method based on neural network
  • 12-lead electrocardiosignal multi-label classification method based on neural network

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[0021] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer, the present invention will be further described in detail in combination with the embodiments and accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. The technical solutions of the present invention will be described in detail below in conjunction with the embodiments and drawings, but the scope of protection is not limited thereto.

[0022] A neural network-based 12-lead electrocardiographic signal multi-label classification method, the specific steps are:

[0023] Such as figure 1 with 2 As shown, the specific input is the data format of 12×1500, that is, the number of leads is 12, and the length is the ECG signal collected by sampling at 500HZ for 3s. The improved convolutional neural network has 3 independent str...

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Abstract

The invention discloses a 12-lead electrocardiosignal multi-label classification method based on a neural network, and belongs to the technical field of computer image processing. The method aims to fully mine and analyze potential features of 12 lead signals by using the neural network, identify normality and heart failure through the signals, and further subdivide the heart failure to find out aspecific heart failure type, so that a doctor can timely diagnose and treat the heart failure according to symptoms. The method comprises the following specific steps: determining a data input format, improving a convolutional neural network, inputting data by fusing feature information of three branches through a full connection layer, and finally classifying labels. The 12 lead signals are usedas a data source to fully mine clinical information of a patient, and the improved three-branch CNN is used for extracting medical features of different scales to find a rule from RNN time serialityof a double-layer LSTM structure, so the multi-classification accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of computer image processing, and relates to a neural network-based multi-label classification method for 12-lead ECG signals. Background technique [0002] Heart failure (HF), referred to as heart failure, is the change of myocardial structure and function caused by initial myocardial injury of any cause (such as myocardial infarction, hemodynamic overload, inflammation, etc.), which in turn leads to ventricular filling. and (or) a group of clinical syndromes caused by ejection disorders. Heart failure is an important part of global chronic cardiovascular diseases, and it is the final stage of various heart diseases. become a major public health problem worldwide. Due to the complex pathogenesis of heart failure and the wide variety of clinical manifestations, it is difficult to obtain generally recognized knowledge, and the type of heart failure cannot be correctly identified, which has a certain degree ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0402A61B5/00G06K9/62
CPCA61B5/7235A61B5/7267A61B5/316A61B5/318G06F18/214G06F18/24G06F18/253
Inventor 李灯熬赵菊敏武行
Owner TAIYUAN UNIV OF TECH
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