阅读说明：本技术 一种基于dwnn框架的心电信号的识别方法 (DWNN framework-based electrocardiosignal identification method ) 是由 包志强 邢瑜 王宇霆 张燕 于 2019-09-25 设计创作，主要内容包括：本发明公开了一种基于DWNN框架的心电信号的识别方法,心电图作为原始数据进入小波层,小波层通过小波分解和随机加权重构,提取出心电图中的深层数据特征,池化层对提取到的数据特征经过池化操作降维,全连接层是将降维的数据特征综合起来,输出层利用softmax函数输出分类结果。在800个测试心电信号中,本发明总共有794个信号预测正确,6个信号预测错误,本发明的预测正确率为99.25％；结果表明本发明具有更为明显的分类识别结果。(The invention discloses an electrocardiosignal identification method based on a DWNN framework, which comprises the steps that an electrocardiogram is used as original data to enter a wavelet layer, deep data features in the electrocardiogram are extracted by the wavelet layer through wavelet decomposition and random weighting reconstruction, dimensionality reduction is carried out on the extracted data features through pooling operation by a pooling layer, the dimensionality reduction data features are integrated by a full connection layer, and a classification result is output by an output layer through a softmax function. In 800 test electrocardiosignals, the prediction accuracy of 794 signals and the prediction error of 6 signals are both correct and wrong, and the prediction accuracy of the invention is 99.25 percent; the result shows that the method has more obvious classification and identification results.)

1. A DWNN framework-based electrocardiosignal identification method is characterized by comprising the following operations:

1) constructing a DWNN framework model comprising a deep feature construction module, a full connection layer and an output layer, wherein the deep feature extraction module comprises n sub-modules consisting of a wavelet layer and a pooling layer, electrocardiosignals alternately enter the wavelet layer and the pooling layer, the wavelet layer extracts deep data features in the electrocardiosignals through wavelet decomposition and random weighted reconstruction, the pooling layer performs pooling dimension reduction on the extracted deep data features, and the deep features of the wavelet structure are obtained after alternate processing; the full connection layer synthesizes deep features of the wavelet structure and then delivers the deep features to the output layer; the output layer outputs the classification probability of the electrocardiosignals by adopting a softmax function;

2) learning and training the constructed DWNN frame model by forward propagation by using a training set, adjusting the network weight of each layer and the weight during wavelet reconstruction by backward propagation, measuring by using a loss function, and minimizing the error between a prediction result and an actual result after multiple iterations;

3) the electrocardiosignals are input into a trained DWNN frame model, the wavelet layer, the pooling layer and the full connection layer are sequentially processed, the output layer outputs the probability of each classification result, and the type with the maximum probability value is used as the result of the classification and identification of the electrocardiosignals.

2. The DWNN framework-based electrocardiographic signal recognition method according to claim 1, wherein the wavelet layer decomposes the input electrocardiographic signal using two-dimensional discrete wavelet transform to obtain a plurality of sub-images; then carrying out random weighting on the sub-images p times and then carrying out inverse wavelet transform to reconstruct p wavelet feature maps, wherein p is an integer greater than or equal to 1;

and the pooling layer is used for performing dimensionality reduction operation on the reconstructed wavelet feature map and removing redundant information.

3. The DWNN framework-based cardiac electrical signal identification method of claim 2, wherein the wavelet layer, when decomposed, decomposes the cardiac electrical image into four sub-images: approximate values, horizontal details, vertical details and diagonal details of the original drawing; wherein, the signal energy is mainly concentrated in the low frequency part, and the high frequency part is the image detail; and then the four sub-images are subjected to wavelet inverse transformation to obtain a wavelet reconstruction image approximate to the original image.

4. A DWNN framework based electrocardiosignal identification method according to claim 1 or 2, wherein the wavelet transform may use an arbitrary n-layer wavelet decomposition when the electrocardiosignal enters the wavelet layer;

the electrocardiosignals alternately enter a wavelet layer and a pooling layer until the data size of the previous layer after pooling can not be subjected to wavelet decomposition any more; every time a wavelet layer is added, the number of reconstructed characteristic maps is s times that of the previous layer, and the number of generated characteristic maps of the last pooling layer is (p × q … × s), wherein p, q, …, s is more than or equal to 1.

5. The DWNN framework-based electrocardiosignal identification method of claim 1, wherein the fully-connected layer contains m neuron numbers, and the fully-connected layer purifies and integrates deep features of wavelet structures and then delivers the deep features to an output layer;

the output layer is classified and identified by adopting a softmax function, the softmax function maps input to real numbers between 0 and 1, and output is the probability of each classification.

6. The DWNN framework-based cardiac electrical signal recognition method of claim 1, wherein the learning training of the DWNN framework model employs the following operations:

1) forward propagation: the forward propagation is the transformation of an input original image signal from a wavelet layer to a softmax layer to realize the automatic extraction and the pre-classification of the characteristics;

2) and (3) back propagation: the network weight value used for regulating each layer is measured by the loss function of the following formula, so that the error between the predicted result and the actual result is minimum;

wherein y is the output of the softmax function, t is the real result, and k is the number of classifications; t is represented by one-hot: the probability value of the correct label is 1, and the probability values of the other labels are 0;

carrying out backward propagation by adopting a gradient descent method, and updating the bias coefficient, the weight of the full-connection layer and the weight of the wavelet reconstruction characteristic diagram when the weight of each layer is corrected;

and (3) training the data set once every iteration, observing the classification effect of the model by using the Loss value, wherein the Loss value is calculated as:

in the formula, P_iAnd representing the corresponding classification probability of the ith image after passing through the classification model.

7. The DWNN frame-based cardiac electrical signal identification method of claim 1, wherein the training set employs five classes of labeled cardiac electrical signals in the MIT-BIH database, including normal cardiac beats, left bundle branch block cardiac beats, right bundle branch block cardiac beats, ventricular premature beats and pacing cardiac beats; 840 heart beat data of each type are respectively selected to be used as a training model, and 160 test data are used as an evaluation model.

8. The DWNN framework-based cardiac electrical signal identification method of claim 1, wherein the output classifications of the output layer include normal beat type, left bundle branch block type, right bundle branch block type, ventricular premature beat type, and atrial premature beat type; and selecting the type with the maximum probability value as a DWNN framework model to input the electrocardiosignals as a classification recognition result.

Technical Field

The invention belongs to the technical field of medical instruments, relates to intelligent classification of electrocardiosignals, and particularly relates to a classification and identification method of electrocardiosignals based on a DWNN framework.

Background

With the rapid development of artificial intelligence, image classification plays an important role in pattern recognition and machine learning. How to automatically extract image features and automatically classify images by using a computer has been developed as one of important research topics in the fields of artificial intelligence and computer vision. Image classification is one of many applications of machine learning in the fields of commerce, medicine, technology, research, finance, and the like. Machine learning is an important direction in the field of artificial intelligence, and with the deep research of neural network algorithms in machine learning, the network algorithms for deep learning are increasingly improved. The application of deep learning methods is becoming more and more popular, such as the fields of image classification, target detection, natural language translation, robot control, and the like. Among them, Convolutional Neural Networks (CNN) bring great promotion and progress to the image classification field.

The wavelet transform has good localization characteristics in a time domain and a frequency domain, has multi-resolution image representation performance, is known as a mathematical microscope for signal analysis, and is a milestone development in the development history of Fourier analysis. Wavelet analysis is also receiving more and more attention from scholars in the fields of signal analysis, speech synthesis, image classification and recognition, information compression, and the like.

How to autonomously learn deep features from the original signal has become a research hotspot. A Haar-CNN model is provided by combining a plurality of classification algorithms of wavelet transform and machine learning classifiers, Zhang Huina and the like, one component of LL is extracted by the model to serve as a main image block extracted by the wavelet transform, characteristics are provided for a subsequent classifier, and although the image classification accuracy is improved, other three detail components of an original image are ignored. Documents D.Gao, Y.Zhu, X.Wang, K.Yan and J.hong, "A FaultDiagnosis Method of Rolling Bearing Based on Complex CWT and CNN,"2018Prognostics and System Health Management reference (PHM-Chongqing), Chongqin, 2018, pp.1101-1105. Williams and the like respectively enter the four sub-images of the wavelet decomposition into a convolutional neural network in a single OR multiple combined mode, and combine all results by using an OR operator to obtain the final classification; Jen-Tzung Chien and the like send the wavelet decomposed sub-feature maps into a nearest neighbor classifier to obtain a good classification effect. The convolutional neural network has the advantage of self-extracting features, Jie Ren and the like automatically extract the features by using CNN, and an SVM (support vector machine) performs classification, so that the resource allocation management in wireless network communication is effectively improved; in addition, l.li, etc. (l.li, j.wu and x.jin, "CNN Denoising for Medical Image Based on Wavelet Domain,"20189th international Conference on Information Technology in Medicine and edition (ITME), Hangzhou,2018, pp.105-109.) perform a multi-layer convolution operation on the Wavelet decomposed subgraph, and finally obtain the denoised artwork through inverse Wavelet transformation; liu et al (p.liu, h.zhang, w.lian and w.zuo, "Multi-Level Wavelet conditional Neural Networks," in IEEE Access, vol.7, pp.74973-74985,2019) use discrete Wavelet decomposition and CNN to extract features alternately, and reconstruct the original image by alternating inverse discrete Wavelet transforms and CNNs, the proposed MWCNNs are effective for tasks such as image processing, JPEG artifact removal, and object classification. In the prior art, the feature maps extracted by wavelet transformation are processed differently and then are sent to a classifier for learning, and tight coupling of the wavelet and the subsequent classifier is not realized.

Disclosure of Invention

The technical problem to be solved by the invention is to provide an electrocardiosignal identification method based on a DWNN framework, which realizes the tight combination of wavelet analysis and a CNN framework and carries out the identification of a two-dimensional electrocardiogram by the framework based on a deep wavelet neural network.

The invention is realized by the following technical scheme:

a DWNN framework-based electrocardiosignal identification method comprises the following operations:

1) constructing a DWNN framework model comprising a deep feature construction module, a full connection layer and an output layer, wherein the deep feature extraction module comprises n sub-modules consisting of a wavelet layer and a pooling layer, electrocardiosignals alternately enter the wavelet layer and the pooling layer, the wavelet layer extracts deep data features in the electrocardiosignals through wavelet decomposition and random weighted reconstruction, the pooling layer performs pooling dimension reduction on the extracted deep data features, and the deep features of the wavelet structure are obtained after alternate processing; the full connection layer synthesizes deep features of the wavelet structure and then delivers the deep features to the output layer; the output layer outputs the classification probability of the electrocardiosignals by adopting a softmax function;

2) learning and training the constructed DWNN frame model by forward propagation by using a training set, adjusting the network weight of each layer and the weight during wavelet reconstruction by backward propagation, measuring by using a loss function, and minimizing the error between a prediction result and an actual result after multiple iterations;

3) the electrocardiosignals are input into a trained DWNN frame model, the wavelet layer, the pooling layer and the full connection layer are sequentially processed, the output layer outputs the probability of each classification result, and the type with the maximum probability value is used as the result of the classification and identification of the electrocardiosignals.

The wavelet layer adopts two-dimensional discrete wavelet transform to decompose the input electrocardiosignals to obtain a plurality of sub-images; then carrying out random weighting on the sub-images p times and then carrying out inverse wavelet transform to reconstruct p wavelet feature maps, wherein p is an integer greater than or equal to 1;

and the pooling layer is used for performing dimensionality reduction operation on the reconstructed wavelet feature map and removing redundant information.

The wavelet layer decomposes the electrocardiogram image into four sub-images during decomposition: approximate values, horizontal details, vertical details and diagonal details of the original drawing; wherein, the signal energy is mainly concentrated in the low frequency part, and the high frequency part is the image detail; and then the four sub-images are subjected to wavelet inverse transformation to obtain a wavelet reconstruction image approximate to the original image.

Furthermore, when the electrocardiosignals enter a wavelet layer, the wavelet transformation can adopt arbitrary n-layer wavelet decomposition;

the electrocardiosignals alternately enter a wavelet layer and a pooling layer until the data size of the previous layer after pooling can not be subjected to wavelet decomposition any more; every time a wavelet layer is added, the number of reconstructed characteristic maps is s times that of the previous layer, and the number of generated characteristic maps of the last pooling layer is (p × q … × s), wherein p, q, …, s is more than or equal to 1.

The full-junction layer contains m neuron numbers, and the full-junction layer purifies and integrates deep features of the wavelet structure and then delivers the deep features to the output layer;

the output layer is classified and identified by adopting a softmax function, the softmax function maps input to real numbers between 0 and 1, and output is the probability of each classification.

The DWNN frame model learning training adopts the following operations:

1) forward propagation: the forward propagation is the transformation of an input original image signal from a wavelet layer to a softmax layer to realize the automatic extraction and the pre-classification of the characteristics;

2) and (3) back propagation: the network weight value used for regulating each layer is measured by the loss function of the following formula, so that the error between the predicted result and the actual result is minimum;

wherein y is the output of the softmax function, t is the real result, and k is the number of classifications; t is represented by one-hot: the probability value of the correct label is 1, and the probability values of the other labels are 0;

carrying out backward propagation by adopting a gradient descent method, and updating the bias coefficient, the weight of the full-connection layer and the weight of the wavelet reconstruction characteristic diagram when the weight of each layer is corrected;

and (3) training the data set once every iteration, observing the classification effect of the model by using the Loss value, wherein the Loss value is calculated as:

in the formula, P_iAnd representing the corresponding classification probability of the ith image after passing through the classification model.

The training set adopts five types of marked electrocardiogram signals in the MIT-BIH database, including normal heart beat, left bundle branch block heart beat, right bundle branch block heart beat, ventricular premature beat and pacing heart beat; 840 heart beat data of each type are respectively selected to be used as a training model, and 160 test data are used as an evaluation model.

Compared with the prior art, the invention has the following beneficial technical effects:

Drawings

Fig. 1 is an overall structure diagram of the DWNN of the present invention, in which the input data size is exemplified by 48 × 48.

FIG. 2 is an overall flow chart of the present invention.

FIG. 3 is a flow chart of the recognition process of the present invention.

FIG. 4 is a diagram of a CNN structure including one convolutional layer.

Fig. 5 is a structural diagram of DWNN.

FIG. 6 shows the Loss values of the CNN and DWNN models with iteration number on the MIT-BIH electrocardiogram data set.

Detailed Description

The present invention will now be described in further detail with reference to the following examples, which are intended to be illustrative, but not limiting, of the invention.

Referring to fig. 1-3 and 5, the identification method of electrocardiosignals based on the DWNN framework provided by the present invention includes the following operations:

1) constructing a DWNN framework model comprising a deep feature construction module, a full connection layer and an output layer, wherein the deep feature extraction module comprises n sub-modules consisting of a wavelet layer and a pooling layer, electrocardiosignals alternately enter the wavelet layer and the pooling layer, the wavelet layer extracts deep data features in the electrocardiosignals through wavelet decomposition and random weighted reconstruction, the pooling layer performs pooling dimension reduction on the extracted deep data features, and the deep features of the wavelet structure are obtained after alternate processing; the full connection layer synthesizes deep features of the wavelet structure and then delivers the deep features to the output layer; the output layer outputs the classification probability of the electrocardiosignals by adopting a softmax function;

2) learning and training the constructed DWNN frame model by forward propagation by using a training set, adjusting the network weight of each layer and the weight during wavelet reconstruction by backward propagation, measuring by using a loss function, and minimizing the error between a prediction result and an actual result after multiple iterations;

3) the electrocardiosignals are input into a trained DWNN frame model, the wavelet layer, the pooling layer and the full connection layer are sequentially processed, the output layer outputs the probability of each classification result, and the type with the maximum probability value is used as the result of the classification and identification of the electrocardiosignals.

Furthermore, the wavelet layer adopts two-dimensional discrete wavelet transform to decompose the input electrocardiosignals to obtain a plurality of sub-images; then carrying out random weighting on the sub-images p times and then carrying out inverse wavelet transform to reconstruct p wavelet feature maps, wherein p is an integer greater than or equal to 1;

and the pooling layer is used for performing dimensionality reduction operation on the reconstructed wavelet feature map and removing redundant information.

Specifically, the wavelet layer decomposes the electrocardiograph image into four sub-images during decomposition: approximate values, horizontal details, vertical details and diagonal details of the original drawing; wherein, the signal energy is mainly concentrated in the low frequency part, and the high frequency part is the image detail; and then the four sub-images are subjected to wavelet inverse transformation to obtain a wavelet reconstruction image approximate to the original image.

Specifically, when the electrocardiosignal enters a wavelet layer, the wavelet transform can adopt arbitrary n-layer wavelet decomposition;

the electrocardiosignals alternately enter a wavelet layer and a pooling layer until the data size of the previous layer after pooling can not be subjected to wavelet decomposition any more; every time a wavelet layer is added, the number of reconstructed characteristic maps is s times that of the previous layer, and the number of generated characteristic maps of the last pooling layer is (p × q … × s), wherein p, q, …, s is more than or equal to 1.

Specific examples are given below.

A DWNN framework-based abnormal electrocardiosignal identification method is characterized by comprising the following operations:

1) construction of DWNN model

1.1) constructing a wavelet layer; decomposing an electrocardio image by adopting two-dimensional discrete wavelet transform to obtain a plurality of sub-images respectively comprising signal energy and image details, and then carrying out wavelet inverse transform on the plurality of sub-images, wherein the weights of the sub-images in the original wavelet inverse transform are all 1.

The pair of electrocardiogram images is decomposed into four sub-images: approximate values, horizontal details, vertical details and diagonal details of the original drawing; wherein the signal energy is mainly concentrated in the low frequency part and the high frequency part is the image detail. Taking a layer of wavelet decomposition as an example, four sub-images comprise one low-frequency image and three high-frequency images.

1.2) building a pooling layer, and entering the reconstructed wavelet feature map into the pooling layer for dimension reduction operation to remove redundant information;

1.3) one wavelet layer and a pooling layer form a sub-module, and n sub-modules form a deep characteristic construction module, so that electrocardiosignals can alternately enter the wavelet layer and the pooling layer for multiple times, and deep characteristics of the wavelet structure are obtained;

in each wavelet layer, the wavelet transform can adopt arbitrary n-layer wavelet decomposition; the wavelet layer and the pooling layer are alternated for multiple times until the size of the data after the previous layer of pooling can not be subjected to wavelet decomposition any more;

every time a wavelet layer is added, the number of the characteristic graphs becomes s times that of the previous layer, and the number of the characteristic graphs generated by the last pooling layer is (p × q … × s), wherein p, q, …, s is more than or equal to 1;

1.4) constructing a full-junction layer, wherein deep features enter the full-junction layer, the full-junction layer contains m neuron numbers, and the full-junction layer highly purifies the previously extracted features and delivers the highly purified features to a final output layer.

1.5) constructing an output layer, wherein the output layer adopts a softmax function to carry out final classification prediction, and for the multi-classification problem, the softmax function can map input into real numbers between 0 and 1, namely the output of the softmax function is the probability of each classification being taken.

Specifically, the softmax function maps the outputs of a plurality of neurons to a (0,1) interval, the probability of belonging to each type of disease is sequentially calculated through the softmax function, the sum of the probabilities is 1, and the type with the highest probability is used as the network prediction output.

2) Learning of DWNN models

Five types of electrocardiogram signals in an MIT-BIH database provided by American Massachusetts institute of technology are selected for training. The five selected labeled types are respectively normal heart beat, left bundle branch block heart beat, right bundle branch block heart beat, ventricular premature beat and pacing heart beat, 840 heart beat data are respectively selected from each type to be used as a training model, and 160 test data are used as evaluation models. Specifically, the MIT-BIH electrocardiogram data set used contained 4200 training images, 800 test images. With each image size being 48 x 48. And recording the Loss value in the training process by using the proposed DWNN training model, and observing the accuracy rate on the test set.

2.1) forward propagation: the forward propagation is the transformation of an input original image signal from a wavelet layer to a softmax layer, and realizes the automatic extraction and the pre-classification of the characteristics; the electrocardiogram is used as original data to enter a wavelet layer, deep data features in the electrocardiogram are extracted by the wavelet layer through wavelet decomposition and random weighted reconstruction, dimensionality reduction is performed on the extracted data features through pooling operation by a pooling layer, dimensionality reduction data features are integrated by a full connection layer, and a classification result and probability are output by an output layer through a softmax function;

2.2) counter-propagating: and the method is used for adjusting the network weight of each layer to minimize a loss function, so that the error between a predicted result and an actual result is minimized.

In the machine learning model, the difference between the predicted value and the true value of a single sample is called loss, the smaller the loss is, the better the model is, and the quality of each prediction of the model is measured by a loss function. There are many loss functions, and specifically, cross entropy is used as the loss function, as shown in formula (1):

where y represents the predicted output of the network, t represents the true result, and k represents the dimensionality of the data. Herein, y is the output of the softmax function, i.e. the probability values belonging to the respective classes; t is denoted by one-hot, i.e. the probability value for the correct tag is 1 and the probability values for the remaining tags are 0. k is the number of actual classifications.

Back propagation adjusts network parameters, the most commonly used method being the gradient descent method. When the weights of all layers are corrected, besides updating the bias coefficient, the weight of the full connection layer and the like, the weights when the wavelet is used for reconstructing the characteristic diagram need to be updated, and the tight coupling of the wavelet and a subsequent network is realized. And obtaining excellent network parameters through multiple iterations.

Specifically, after forward propagation of the network is realized, an output value of each node of each layer is obtained, then an optimization function of the network is determined, residuals of a predicted value and a real value of a sample are calculated in the output layer, residuals of each node of other layers are calculated respectively, finally partial derivatives of the optimization function to a weight and a bias are calculated respectively, and the weight and the bias are updated according to a gradient descent method. When the reconstruction weight of the wavelet layer is updated, the residual error is the corresponding wavelet decomposition value of the residual error transmitted from the pooling layer in the reverse direction.

And training a wavelet characteristic diagram which is more beneficial to classification by continuously iterating by using a gradient descent method so as to minimize the error between the output value and the target value. And (3) training the data set once every iteration, observing the classification effect of the model by using the Loss value, wherein the Loss value is calculated as shown in the formula (2):

in the formula (2), P_iThe classification probability of the ith image corresponding to its label after passing through the classification model is shown as follows: p_iThe larger the Loss value, the smaller the Loss value. As the iteration times are increased, the Loss value is smaller and smaller when the classification accuracy of each graph is continuously improved. The evaluation index shows whether the continuously updated parameters in the model can enable the classification effect of the model to be better.

After the model training is completed, the classification accuracy on the test set is generally represented by acaury, and the value of the classification accuracy on the test set is equal to the number of correctly classified pictures on the test set divided by the total number of pictures in the test set. This evaluation index indicates the generalization ability of the model.

3) Classification identification of electrocardiogram

The trained network model is used as a network for automatically identifying unknown electrocardiosignals, the electrocardiosignals of the test set are input into the trained network, the processing of a wavelet layer, a pooling layer and a full connection layer is carried out in sequence, the probability of each classification result is output by a softmax function of an output layer, and the type with the maximum probability value is used as the result of the classification of the electrocardiosignals.

The schematic diagram of the DWNN framework proposed by the present invention is shown in fig. 5. The wavelet layer is decomposed by adopting a layer of wavelet to obtain four sub-images containing different component information; during weighted reconstruction, taking 20 from p to obtain 20 wavelet feature maps; m is 50 and k is selected to be 5 according to the MIT-BIH data set. The structure generates 20 characteristic graphs through primary wavelet decomposition, primary wavelet weighted reconstruction and primary average pooling, and the characteristic graphs are sent to a full connection layer and a softmax layer to obtain a final classification result. After 120 iterations, the LOSS value for the training set was 168.4. Among 800 electrocardiosignals tested, 794 signals are correctly predicted in total, 6 signals are incorrectly predicted, and the prediction accuracy of the model is 99.25%.

In the compared CNN structure, 20 characteristic graphs need to be extracted by using the convolution layer once, then the CNN structure enters average pooling, the number of neurons in the full-link layer is 50, and finally the classification result is obtained by the softmax layer. A schematic diagram of the comparative CNN structure is shown in fig. 4. The size of the convolution kernel is chosen to be 5 x 5 with a step size of 1. After 120 iterations, the LOSS value of the training set was 273.4. In 800 electrocardiosignals tested, 763 signals are correctly predicted and 37 signals are incorrectly predicted, and the prediction accuracy of the model is 95.38%.

In both models, the convergence step size was chosen to be 0.0005, and the number of iterations of the data set when training the model was 120. Comparing the Loss value and the classification accuracy of the CNN model and the DWNN model, the result is shown in Table 1, and the variation of the Loss value with the iteration number iter is shown in FIG. 6. During the first iteration, the CNN has a smaller Loss value, after 120 iterations, the Loss value of the DWNN1 model is 168.4, while the Loss value of the CNN model is 273.4, obviously, the DWNN1 has a smaller Loss value; on the classification accuracy of the two models, the classification accuracy of the CNN is 95.38%, the classification accuracy of the DWNN1 is 99.25%, and the classification accuracy of the DWNN model is obviously higher than that of the CNN model.

TABLE 1 specific Loss values and Classification accuracies for CNN and DWNN1 models

Model (model)	CNN	DWNN1
			Loss value (first iteration)	6736.7	6789.1
Loss value (120 iterations)	273.4	168.4
			acaury (test collection)	95.38％	99.30％

Therefore, the DWNN framework model provided by the invention replaces the convolution layer for extracting the characteristics in the CNN with wavelet decomposition and weighted wavelet reconstruction, realizes the tight coupling of the wavelet and the CNN framework, also achieves the effect of learning deep characteristics and is beneficial to classification. Along with the increase of the iteration times, the Loss value is reduced, and meanwhile, the accuracy rate of image classification is improved.

Through comparison of the two network models on an electrocardiogram data set, the LOSS value of the network model after 120 iterations is smaller than that of a convolutional neural network, the accuracy rate of a test set is higher than that of the convolutional neural network, and the network model has better results.

The embodiments given above are preferable examples for implementing the present invention, and the present invention is not limited to the above-described embodiments. Any non-essential addition and replacement made by the technical characteristics of the technical scheme of the invention by a person skilled in the art belong to the protection scope of the invention.