CN111613321A - Electrocardiogram stroke auxiliary diagnosis method based on dense convolutional neural network - Google Patents
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Abstract
The invention provides an electrocardiogram stroke auxiliary diagnosis method based on a dense convolutional neural network. The method comprises the steps of firstly establishing an initial database, and distributing a training set and a test set; inputting the training set serving as original training data into a dense convolutional neural network for parameter training to obtain a basic electrocardiogram stroke diagnosis model; and finally, inputting the test set serving as input data into the obtained electrocardiogram stroke diagnosis model for recognition to obtain a recognition result. The invention provides a deep neural network which eliminates artificial constraints and reduces subjective factors, so that a model can automatically extract features and explore the relation between stroke and electrocardiogram, not only can automatically extract special features related to stroke classification, but also can well save computer resources, and can meet the requirements on precision.
Description
Technical Field
The invention relates to the field of electrocardiogram signal research and stroke auxiliary diagnosis, in particular to an electrocardiogram stroke auxiliary diagnosis method based on a dense convolutional neural network.
Background
Stroke is the second leading cause of death and accounts for 11.1% of the total mortality in the world. There are a number of mechanisms that lead to stroke, such as microvascular disease, atrial fibrillation and heart disease. Since the brain relies on oxygen and blood glucose delivered through the circulatory system, interruption of blood supply will lead to microvascular disease, resulting in stroke and dementia. From a system perspective, human organs are functionally related to each other. The brain and heart are two important organs, and dysfunction of either of them affects the function of the other. Heart diseases may respond to brain diseases, and electrocardiograms are a common method of diagnosing many heart diseases, so that the data set relating to electrocardiograms is sufficient. A large data set is virtually impossible for a physician to review macroscopically at one time, and thus there is great potential for study in the data information of an electrocardiogram. Therefore, there is a need to design a computer-aided diagnosis (CAD) system to automatically detect abnormal heartbeats.
To date, most CAD used for classification of cardiac electrical signals relies solely on manual extraction of features from the cardiac electrical signals, including Heart Rate Variability (HRV) indicators, morphological features (P-wave absence, QRS interval prolongation). Traditional machine learning-based methods such as linear discriminant analysis and support vector machine are used to extract specific patterns for disease diagnosis, which can save time for doctors and reduce health cost. These modalities, however, rely on the experience of the physician and the individual's diagnostic philosophy. The study divides the electrocardiogram into individual heartbeats and ignores the connection between heartbeats, assuming that each heartbeat contains only one possibility of arrhythmia. Among these manual feature extraction methods, Support Vector Machines (SVMs) and Hidden Markov Models (HMMs) exhibit better performance. However, the accuracy of these methods is affected by the quality of the manually extracted features. The characteristics of the electrocardiosignals have strong subject dependence, and deeper domain knowledge and professional knowledge are needed for extracting effective characteristics. Therefore, it is necessary to extract the most representative and relevant features for electrocardiographic detection.
In recent years, deep learning neural networks have generated a great deal of interest in multi-dimensional signal processing because they can automatically learn features such as classification and time-series data analysis in computer vision. Deep learning helps to extract hierarchical features from large data in the biomedical field. For example, a multi-channel Electromyogram (EMG) pattern is identified using a depth belief network enhancement algorithm, and a non-stationary electroencephalogram (EEG) signal is identified using a superposition self-encoder (SAE) and Principal Component Analysis (PCA). Residual networks based on Convolutional Neural Networks (CNN) use the MIT ECG database to classify 30s single-lead electrocardiograms into 14 classes, where only the raw data is needed to learn the representative features iteratively. The previous work focuses on semantic information of electrocardiographic data, such as feature extraction or heartbeat type detection (normal, ventricular or supraventricular ectopy, fusion, etc.).
Disclosure of Invention
The invention aims to provide an electrocardiogram stroke auxiliary diagnosis method based on a dense convolutional neural network. The method can utilize the electrocardiogram to play a role in auxiliary diagnosis of the stroke to a certain extent, and obtains the result with the training precision of 99.99 percent and the prediction precision of 85.82 percent through fine adjustment of the model. Compared with the traditional algorithm which needs manual division of features, the method not only can automatically extract the special features related to stroke classification, but also can well save computer resources, and can meet the requirements on precision.
In order to achieve the purpose, the invention adopts the technical scheme that:
an electrocardiogram stroke auxiliary diagnosis method based on a dense convolutional neural network comprises the following steps:
step (1), establishing an initial database, and distributing a training set and a testing set.
The method comprises the following steps of performing initial offset correction on an electrocardiogram by arranging and adding modification to relevant 12-lead electrocardiograms provided by a hospital, specifically deleting pictures with unobvious electrocardiogram waveforms; and according to the existence of stroke, marking 12-lead electrocardiogram with corresponding labels, wherein the label with stroke phenomenon is 1, the label without stroke phenomenon is 0, integrating 12-lead electrocardiogram data and the corresponding labels to obtain an initial database, and dividing the established data set in a random division manner to obtain a training set and a test set;
and (2) inputting the training set serving as original training data into a dense convolutional neural network for parameter training to obtain a basic electrocardiogram stroke diagnosis model, wherein the specific method comprises the following steps:
inputting the training set as original data into a dense convolutional neural network, and performing parameter training by the dense convolutional neural network in a supervised learning mode;
firstly, unifying the image size of related 12-lead electrocardiogram in the training set into 256 × 256, inputting the processed training set data into the initial convolution layer to obtain the characteristic diagram output x0Then x is added0As input to the first dense network, an output x of the first dense network is obtained1Said first dense network comprising 5 dense blocks, then x1After the average pooling operation is carried out, the x after the average pooling operation is carried out1As input to the second dense network, an output x of the second dense network is obtained2Said second dense network comprising 5 dense blocks, then x2After the average pooling operation is carried out, the x after the average pooling operation is carried out2As input to the third dense network, an output x of the third dense network is obtained3Said third dense network comprising 4 dense blocks, the output x of the third dense network to be obtained3Performing batch normalization operation, extracting sample characteristics through a correction linear unit, performing average pooling operation, performing size normalization operation, inputting the normalized sample characteristics into a full-connection layer, and obtaining an output result by using a sigmoid function to obtain a basic electrocardiogram stroke diagnosis model;
in order to prevent overfitting caused by small data quantity, the Dense network Dense Net adopts a characteristic reuse method, wherein each Dense network comprises 4-5 Dense blocks Dense Block; the operation of each dense block comprises Batch Normalization, modified linear unit ReLU, 3 × 3convolution layer and regularization operation Dropout, and the feature output by each dense block is used as a part of feature of each layer for feature merging;
the Batch Normalization enables the extracted feature mean value to be 0 and the variance to be 1, so that the subsequent calculation is facilitated; the modified linear unit ReLU is used for extracting sample features;
the initial convolutional layer is used for carrying out convolution on input original data to obtain an original characteristic diagram; the dense blocks are used for reusing features, and the utilization efficiency of information and gradient in a network is improved;
and (3) inputting the test set serving as input data into the obtained electrocardiogram stroke diagnosis model for recognition to obtain a recognition result.
Firstly, unifying the size of the related 12-lead electrocardiogram images in the test set into 256 × 256, and inputting the processed test set data into an electrocardiogram stroke diagnosis model to obtain a recognition result.
The establishment of the initial database refers to the establishment of a corresponding data set according to requirements on the basis of relevant research on electrocardiogram stroke data provided by a hospital;
the internal structure of the Dense network Dense Net is realized as follows:
at input y0In the case of (2), first, y is0Inputting the data into a first dense block, performing Batch Normalization operation through Batch Normalization, extracting sample characteristics by using a modified linear unit ReLU, and finally performing 3 × 3convolution and regularization dropout operation to obtain output y1', will y0And y1' superposition to get input y of the next dense block1The input of each subsequent dense block is the superposition of the input and the output of the previous dense block.
Compared with the prior art, the invention has the following beneficial effects:
firstly, the traditional stroke diagnosis needs electroencephalogram related research and a professional doctor needs to mark for a long time in the aspect of feature extraction to be identified by a traditional algorithm, and different from the traditional feature extraction algorithm, the invention provides a deep neural network which eliminates artificial constraint and reduces subjective factors, so that a model can automatically extract features and explore the relationship between stroke and electrocardiogram.
Secondly, the invention realizes the classification precision of 99.99% on the training set and the classification precision of 85.82% on the testing set. The detection performance can be further improved as the amount of data increases in the future. And the present invention uses the entire electrocardiogram data image as input to ensure correlation between detected heartbeats. The network architecture uses dense blocks to extract features and shows significant performance on a limited set of data. The depth of the network may be appropriately deepened as the amount of data increases. The 85.82% accuracy of stroke detection means that the electrocardiogram can be used as an auxiliary means for stroke diagnosis to some extent.
Thirdly, the invention adopts a Dense convolutional neural network and a Dense Block operation, which means that each layer can be directly connected with input information and gradient information, and all layers are directly connected on the premise of ensuring maximum information transmission between the layers in the network. Therefore, gradient disappearance is reduced, the situation that features are insufficient due to small data input is avoided, and input information is utilized more effectively. Meanwhile, the connection method enables the transmission of the characteristic information and the gradient information to be more effective, the network is easier to train, and the computer resources can be reasonably utilized. In addition to this, the presence of a Connection such as Dense Connection has a regularizing effect, which, in addition to the above-mentioned reduction in the number of parameters, has a very good suppression of the over-fitting phenomenon. Meanwhile, the feature reuse operation used in the operation is different from the jump link adopted in other segmentation networks, such as Tiramisu networks, other jump links mainly focus on connecting the features obtained by downsampling in the process of upsampling, specifically, the features obtained by upsampling can be integrated corresponding to the features obtained by downsampling, and in DenseNet, on one hand, the operation of upsampling is not performed, on the other hand, the connection used is mainly to perform feature retention after each layer of convolution, namely, the last layer retains the features of all downsampling layers before.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a general block diagram of the dense convolutional neural network of the present invention;
fig. 3 is a schematic diagram of the internal structure of the Dense Net according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned and other features and advantages of the invention more comprehensible, embodiments accompanied with figures are described in detail below.
The invention provides an electrocardiogram stroke auxiliary diagnosis method based on a dense convolutional neural network, which comprises a training phase and an identification phase, and is shown in figure 1; the training phase comprises the following steps:
firstly, building a required data set. In the embodiment, 98 related 12-lead electrocardiograms are obtained to serve as an initial database of an electrocardiogram stroke auxiliary diagnosis task, wherein the data ratio of stroke to stroke is 1: 1; and finding out the label information corresponding to the original information matching.
And secondly, inputting 90 pictures into the dense convolutional neural network as a training set to train network parameters. The dense network of the dense neural network is mainly an addition operation using features, in particular according to XL＝HL([x0,x1,x2..xL-1]). Wherein HLIncluding Batch Normalization, ReLU, 3 × 3 Normalization and Dropout, x0,x1,x2..xL-1For the raw data, XLIs passing through HLThe latter output information, the dense network is shown in FIG. 3, the whole convolutional neural network structure is shown in FIG. 2, and a 12-lead electrocardiogram is used as inputUnifying the size of the input image into 256 x 256, and obtaining a feature map output x through an initial convolution layer0Then x is added0As input to the first dense network, an output x of the first dense network is obtained1Said first dense network comprising 5 dense blocks, then x1After the average pooling operation is carried out, the x after the average pooling operation is carried out1As input to the second dense network, an output x of the second dense network is obtained2Said second dense network comprising 5 dense blocks, then x2After the average pooling operation is carried out, the x after the average pooling operation is carried out2As input to the third dense network, an output x of the third dense network is obtained3Said third dense network comprising 4 dense blocks, the output x of the third dense network to be obtained3And performing batch normalization operation, extracting sample characteristics through a modified linear unit, performing average pooling operation, performing size normalization operation, inputting the normalized sample characteristics into a full-connection layer, and obtaining an output result by using a sigmoid function.
Wherein the Batch Normalization enables the extracted feature mean value to be 0 and the variance to be 1, so that the subsequent calculation is facilitated; 3. the constraint operation of the 3 x 3 reduces the number of input feature maps, thereby reducing the calculated amount in dimension reduction and fusing the features extracted by each channel; the ReLU is used to extract sample features.
Please refer to fig. 3, which is a schematic diagram of an internal structure of a Dense network density Net according to an embodiment of the present invention. The input is y0In the case of (2), first, y is0Inputting the data into a first dense block, performing Batch Normalization operation through Batch Normalization, extracting sample features by using a modified linear unit ReLU, and finally performing 3-by-3 convolution operation and regularization Dropout to obtain an output y1', will y0And y1' performing channel superposition, i.e. changing the number of channels instead of adding weights without changing the number of channels, results in the input y of the next dense block1The input of each subsequent dense block is the superposition of the input and the output of the previous dense block.
Training dense convolutional nervesWhen the network is in use, the network is trained by adopting a supervised learning mode of ascending from bottom to top and randomly initializing weights, and default parameters β are used10.9 and β2Adam optimizer 0.999, batch size 9. The learning rate is set to 0.001. Firstly, taking preprocessed pictures and data as input to train a first hidden layer (namely inputting the preprocessed pictures and the data into an initial convolutional layer), and firstly learning parameters of a dense convolutional neural network during training; furthermore, due to the limitation of the network, sparsity constraint and constraint of prior conditions, the network structure obtains the characteristic with more representation capability than the data per se; after learning to obtain the n-1 th layer, the output of the n-1 th layer is used as the input of the n-1 th layer, and the n-th layer is trained, thereby obtaining the parameters of each layer.
And saving the adjusted parameters to obtain a relevant model.
Step one, test data (8 pictures) obtained randomly before are used as testing to test the accuracy of the model obtained in the training stage, and the result is stored;
and secondly, inputting the preprocessed data or the test data into the obtained model for recognition to obtain a recognition result corresponding to the test set.
The above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and a person skilled in the art can make modifications or equivalent substitutions to the technical solution of the present invention without departing from the spirit and scope of the present invention, and the scope of the present invention should be determined by the claims.
Claims (8)
1. An electrocardiogram stroke auxiliary diagnosis method based on a dense convolutional neural network is characterized by comprising the following steps:
step (1), establishing an initial database, and distributing a training set and a test set;
inputting the training set serving as original training data into a dense convolutional neural network for parameter training to obtain a basic electrocardiogram stroke diagnosis model;
step (3), inputting the test set as input data into the obtained electrocardiogram stroke diagnosis model for recognition to obtain a recognition result;
firstly, unifying the size of the related 12-lead electrocardiogram images in the test set into 256 × 256, and inputting the processed test set data into an electrocardiogram stroke diagnosis model to obtain a recognition result.
2. The electrocardiogram stroke auxiliary diagnosis method based on the dense convolutional neural network as claimed in claim 1, wherein the step (1) of establishing an initial database, and distributing a training set and a testing set comprises the following specific steps:
the method comprises the following steps of performing initial offset correction on an electrocardiogram by arranging and adding modification to relevant 12-lead electrocardiograms provided by a hospital, specifically deleting pictures with unobvious electrocardiogram waveforms; and according to the fact that the 12-lead electrocardiogram is marked with the corresponding label in the process of stroke, wherein the label with the stroke phenomenon is 1, the label without the stroke phenomenon is 0, the 12-lead electrocardiogram data and the corresponding label are integrated to obtain an initial database, and the established data set is divided in a random dividing mode to obtain a training set and a testing set.
3. The electrocardiogram apoplexy auxiliary diagnosis method based on the dense convolutional neural network as claimed in claim 2, wherein the step (2) is to input the training set as the original training data into the dense convolutional neural network for parameter training to obtain the basic electrocardiogram apoplexy diagnosis model, and the specific method is as follows:
inputting the training set as original data into a dense convolutional neural network, and performing parameter training by the dense convolutional neural network in a supervised learning mode;
firstly, unifying the image size of related 12-lead electrocardiogram in the training set into 256 × 256, inputting the processed training set data into the initial convolution layer to obtain the characteristic diagram output x0Then x is added0As input to the first dense network, an output x of the first dense network is obtained1Said first dense network comprising 5 dense blocks, then x1After the average pooling operation is carried out, the x after the average pooling operation is carried out1As input to the second dense network, an output x of the second dense network is obtained2Said second dense network comprising 5 dense blocks, then x2After the average pooling operation is carried out, the x after the average pooling operation is carried out2As input to the third dense network, an output x of the third dense network is obtained3Said third dense network comprising 4 dense blocks, the output x of the third dense network to be obtained3And performing batch normalization operation, then extracting sample characteristics through a correction linear unit, performing average pooling operation, inputting the sample characteristics into a full-connection layer after performing size normalization operation, and obtaining an output result by using a sigmoid function to obtain a basic electrocardiogram stroke diagnosis model.
4. The electrocardiogram stroke auxiliary diagnosis method based on the Dense convolutional neural network as claimed in claim 3, wherein the Dense network Dense Net adopts a feature reuse method to prevent overfitting caused by small data volume, wherein each Dense network comprises 4-5 Dense blocks Dense Block; the operation of each dense block comprises Batch Normalization, modified linear unit ReLU, 3 × 3convolution layer and regularization operation Dropout, and the feature output by each dense block is combined as a part of the feature of each layer.
5. The intensive convolutional neural network-based electrocardiogram stroke auxiliary diagnosis method as claimed in claim 4, wherein the Batch Normalization makes the extracted feature mean 0 and variance 1, which is convenient for the subsequent calculation; the modified linear unit ReLU is used for extracting sample features.
6. The electrocardiogram stroke auxiliary diagnosis method based on the dense convolutional neural network as claimed in claim 5, wherein the initial convolutional layer is used for performing convolution on the input raw data to obtain a raw feature map; the dense blocks are used for feature reuse, and utilization efficiency of information and gradients in a network is improved.
7. The method as claimed in claim 6, wherein the initial database is created by constructing a corresponding data set according to requirements based on the investigation on the stroke data provided by hospitals.
8. The electrocardiogram stroke auxiliary diagnosis method based on the Dense convolutional neural network as claimed in claim 7, wherein the internal structure of the Dense network Dense Net is realized as follows:
at input y0In the case of (2), first, y is0Inputting the data into a first dense block, performing Batch Normalization operation through Batch Normalization, extracting sample characteristics by using a modified linear unit ReLU, and finally performing 3 × 3convolution and regularization dropout operation to obtain output y1', will y0And y1' superposition to get input y of the next dense block1The input of each subsequent dense block is the superposition of the input and the output of the previous dense block.
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