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An ECG signal recognition method based on dwnn framework

A technology of electrocardiographic signal and identification method, which is applied in the classification and identification of electrocardiographic signals, and the field of intelligent classification of electrocardiographic signals, and can solve the problems of unrealized tight coupling between wavelet and subsequent classifiers, etc.

Active Publication Date: 2022-05-24
XIAN UNIV OF POSTS & TELECOMM
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Problems solved by technology

The above existing technologies all process the feature maps extracted by wavelet transform differently, and then send them to the classifier for learning, and do not realize the tight coupling between wavelet and subsequent classifiers

Method used

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  • An ECG signal recognition method based on dwnn framework
  • An ECG signal recognition method based on dwnn framework
  • An ECG signal recognition method based on dwnn framework

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Embodiment Construction

[0037] The present invention will be described in further detail below with reference to the embodiments, which are to explain rather than limit the present invention.

[0038] see Figure 1-Figure 3 , Figure 5 , the method for identifying ECG signals based on the DWNN framework provided by the present invention includes the following operations:

[0039] 1) Construct a DWNN framework model including a deep feature construction module, a fully connected layer and an output layer, in which the deep feature extraction module includes n sub-modules composed of wavelet layers and pooling layers, and the ECG signal alternately enters the wavelet layer and the pooling layer. The wavelet layer extracts the deep data features in the ECG signal through wavelet decomposition and random weighted reconstruction, and the pooling layer performs pooling and dimension reduction on the extracted deep data features, and after alternate processing, the deep features of the wavelet structure ar...

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Abstract

The invention discloses a method for recognizing electrocardiogram signals based on a DWNN framework. The electrocardiogram enters the wavelet layer as the original data, and the wavelet layer extracts deep data features in the electrocardiogram through wavelet decomposition and random weighting reconstruction, and the pooling layer pairs extract The obtained data features are reduced in dimension through pooling operation. The fully connected layer combines the reduced data features, and the output layer uses the softmax function to output the classification results. Among the 800 test ECG signals, the present invention predicts 794 signals correctly and 6 signals incorrectly, and the prediction accuracy rate of the present invention is 99.25%. The result shows that the present invention has more obvious classification recognition results.

Description

technical field [0001] The invention belongs to the technical field of medical devices, and relates to the intelligent classification of electrocardiographic signals, in particular to a method for classifying and identifying electrocardiographic signals based on a DWNN framework. Background technique [0002] With the rapid development of artificial intelligence, image classification plays an important role in pattern recognition and machine learning. How to use computer to automatically extract image features and automatically classify images has developed into one of the important research topics in the field of artificial intelligence and computer vision. Image classification is one of many applications of machine learning in business, medicine, technology, research, finance, and more. Machine learning is an important direction in the field of artificial intelligence. With the in-depth study of neural network algorithms in machine learning, the network algorithms of deep...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/318A61B5/00
CPCA61B5/7264A61B5/7225A61B5/7203A61B5/369
Inventor 包志强邢瑜王宇霆张燕
Owner XIAN UNIV OF POSTS & TELECOMM
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