Electrocardiogram classification method and device based on wavelet transform and DCNN (Deep Convolutional Neural Networks)

Pending Publication Date: 2020-05-29
QILU UNIV OF TECH
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

In view of the weak ECG signal and the insufficient level of extracted features leading to insuffic

Method used

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  • Electrocardiogram classification method and device based on wavelet transform and DCNN (Deep Convolutional Neural Networks)
  • Electrocardiogram classification method and device based on wavelet transform and DCNN (Deep Convolutional Neural Networks)
  • Electrocardiogram classification method and device based on wavelet transform and DCNN (Deep Convolutional Neural Networks)

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Embodiment 1

[0064] see figure 1 , an electrocardiogram classification method based on wavelet transform and DCNN in the present invention, the method first adopts wavelet transform to filter the electrocardiogram signal, and locates the analysis on the time and frequency of the electrocardiogram signal, and through the stretching and translation operation, the signal is gradually Multi-scale refinement effectively retains the eigenvalues ​​of the signal. Secondly, the deep convolutional neural network is used to extract the hierarchical features of the ECG signal. Finally, the test is performed on the test data set, and the classifier is used for classification. The implementation includes the following steps:

[0065] Step S1, select data set

[0066] Due to the characteristic that the electrocardiogram signal is susceptible to interference, the present invention selects the electrocardiogram data set provided by the 2017 PhysioNet / CINC Challenge as the judge. 2017 PhysiNet / CINC challen...

Embodiment 2

[0102] as attached figure 1 Shown, a kind of electrocardiogram classification device based on wavelet transform and DCNN of the present invention comprises:

[0103] The data set sampling module selects the data set and samples the electrocardiogram data in the data set;

[0104] The data set preprocessing module uses wavelet transform to preprocess the ECG data in the data set to obtain ECG data test samples;

[0105] The deep convolution and feature extraction module uses the deep convolutional neural network DCNN to extract the hierarchical features of the ECG signal, and uses a larger convolution kernel than the extracted image features to expand the perception field of convolution and at the same time use one-dimensional convolution Accumulate kernel to extract features of ECG signal;

[0106] Classifier module: use the classifier to predict the ECG data test samples and output the classification results;

[0107] Evaluation indicator module: evaluate the classificatio...

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Abstract

The invention provides an electrocardiogram classification method and device based on wavelet transform and DCNN (deep convolutional neural networks), and belongs to the field of biological medicinesand modes. According to an automatic classification method and device of electrocardiogram signals based on the wavelet transform and the deep convolutional neural networks, by utilizing wavelet functions, the electrocardiogram signals are decomposed into sub-signals of different frequency scales, and the sub-signals are filtered in sectional modes and are subjected to wavelet reconstruction; by utilizing 24 layers of convolutional neural networks, characteristic extraction is carried out by adopting crossed-size convolution kernels, data overfitting is prevented by adopting dropout and BatchNormalization when characteristic information is transmitted; and finally, classification is carried out by adopting a softmax classifier. The method is already verified on an ECG data set provided by2017 PhysioNet/CinC Challenge, and the accuracy rate is 0.871, and an F1 score is 0.8652. A research proves that noises of the electrocardiogram signals can be eliminated more effectively through thewavelet transform, and extraction of multi-layer characteristics can be performed by utilizing the 24 layers of convolutional neural networks, and meanwhile, a receptive field can be improved by increasing the sizes of the convolution kernels, so that the classification performance of a model is improved.

Description

technical field [0001] The invention relates to the fields of biomedicine and pattern recognition, in particular to an electrocardiogram classification method and device based on wavelet transform and DCNN. Background technique [0002] Atrial fibrillation (AF) is the most common sustained tachyarrhythmia in clinical practice. The main hazard of atrial fibrillation is the increased risk of vascular embolism, which is one of the main causes of ischemic stroke. Atrial fibrillation is manifested on the electrocardiogram as the disappearance of sinus P waves in each lead, the shape and amplitude of QRS waves are basically the same as sinus rhythm, and the R-R interval is absolutely unbalanced. The ECG automatic analysis and classification system can provide great help to doctors in diagnosing heart diseases, and is of great significance to improving medical efficiency, reducing medical costs, and preventing and diagnosing heart diseases. [0003] In recent years, extensive res...

Claims

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Application Information

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IPC IPC(8): A61B5/0402A61B5/00G06K9/00G06K9/62G06N3/04G06N3/08
CPCA61B5/7264A61B5/725A61B5/726A61B5/7203G06N3/08A61B5/318G06N3/045G06F2218/06G06F18/2414
Inventor 成金勇赵运祥张平
Owner QILU UNIV OF TECH
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