CN112932497A - Unbalanced single-lead electrocardiogram data classification method and system - Google Patents
Unbalanced single-lead electrocardiogram data classification method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for classifying unbalanced single-lead electrocardiogram data, wherein the method comprises the following steps: acquiring an original electrocardiosignal and preprocessing the original electrocardiosignal to obtain a preprocessed electrocardiosignal; carrying out depth feature extraction on the preprocessed electrocardiosignals and reducing dimensions of the depth features to obtain a majority class of features and a minority class of features; synthesizing the minority class of features based on the SMOTE algorithm to obtain a minority class of feature synthesis sample; and performing SVM classification on the original electrocardiosignals according to the majority class characteristics and the minority class characteristics to synthesize samples so as to obtain a classification result. The system comprises: the device comprises a preprocessing module, a feature extraction module, a sample synthesis module and a classification module. By using the method and the device, the depth characteristics of the ECG information can be deeply mined for generating the sample so as to improve the classification accuracy of the model. The method and the system for classifying the unbalanced single-lead electrocardiogram data can be widely applied to the field of signal classification.
Description
Technical Field
The invention relates to the field of signal classification, in particular to a method and a system for classifying unbalanced single-lead electrocardiogram data.
Background
Currently, deep learning methods have been successful in various fields such as target detection, medical image anomaly detection and electrocardiogram intelligent diagnosis, but most of such methods are constructed based on balanced data, and when the methods are faced with real unbalanced data, the methods are severely limited, especially in the medical field, such as the electrocardiogram intelligent auxiliary diagnosis based on deep learning, with the development of wearable equipment and the internet of things, a patient can acquire electrocardiogram signals at any time, and when the quantity ratio of normal electrocardiogram signals to abnormal electrocardiogram signals is seriously unbalanced, the electrocardiogram intelligent auxiliary algorithm is easily misclassified, and a small number of abnormal electrocardiogram signals are easily misclassified into normal electrocardiogram signals.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an unbalanced single-lead electrocardiogram data classification method and system, which combines a convolutional neural network with a few synthesis oversampling technologies to classify unbalanced electrocardiogram data sets and improve the model classification effect.
The first technical scheme adopted by the invention is as follows: an unbalanced single-lead electrocardiogram data classification method comprises the following steps:
acquiring an original electrocardiosignal and preprocessing the original electrocardiosignal to obtain a preprocessed electrocardiosignal;
carrying out depth feature extraction on the preprocessed electrocardiosignals and reducing dimensions of the depth features to obtain a majority class of features and a minority class of features;
synthesizing the minority class of features based on the SMOTE algorithm to obtain a minority class of feature synthesis sample;
and performing SVM classification on the original electrocardiosignals according to the majority class characteristics and the minority class characteristics to synthesize samples so as to obtain a classification result.
Further, the step of obtaining an original electrocardiographic signal and preprocessing the original electrocardiographic signal to obtain a preprocessed electrocardiographic signal specifically includes:
acquiring an original electrocardiosignal;
carrying out six-layer decomposition on the original electrocardiosignals based on discrete wavelet transform to obtain high-frequency components and low-frequency components of corresponding layers;
discarding the first layer high-frequency component, the second layer high-frequency component and the sixth layer low-frequency component, and reconstructing the remaining high-frequency component and low-frequency component to obtain a reconstructed electrocardiosignal;
and cutting and zero padding are carried out on the reconstructed electrocardiosignals, and the length of the reconstructed electrocardiosignal data is controlled to be 30s, so that the preprocessed electrocardiosignals are obtained.
Further, the step of performing depth feature extraction on the preprocessed electrocardiosignals and performing dimension reduction on the depth features to obtain a majority class of features and a minority class of features specifically includes:
performing depth feature extraction on the preprocessed electrocardiosignals based on a pre-constructed convolutional neural network to obtain 64-dimensional features corresponding to a plurality of samples and a plurality of samples;
and performing dimensionality reduction processing on 64-dimensional features corresponding to the majority samples and the minority samples based on a PCA dimensionality reduction algorithm to obtain 41-dimensional majority features and minority features.
Further, the pre-constructed convolutional neural network comprises four convolutional layers, three maximum pooling layers and three full-connected layers.
Further, the step of synthesizing a minority class of features based on the SMOTE algorithm to obtain a minority class of feature synthesis sample specifically includes:
for each 41-dimensional minority feature X, calculating the distance from X to all other features by taking the Euclidean distance as a standard to obtain corresponding k neighbors;
setting a sampling ratio and determining a sampling multiple N according to the unbalanced ratio of the majority class features and the minority class features;
for each minority class of features X, randomly selecting a plurality of features from the corresponding k neighbors according to the sampling multiple N, and selecting neighbor Xn;
and reconstructing the selected neighbor Xn to obtain a few class characteristic synthetic samples.
Further, the formula for reconstructing the sample for the selected neighbor Xn is as follows:
in the above formula, rand (0,1) represents randomly selecting an array from 0-1, X represents a few kinds of features,
representing the selected K neighbors.
The second technical scheme adopted by the invention is as follows: an unbalanced single lead electrocardiographic data classification system comprising:
the preprocessing module is used for acquiring an original electrocardiosignal and preprocessing the original electrocardiosignal to obtain a preprocessed electrocardiosignal;
the feature extraction module is used for carrying out depth feature extraction on the preprocessed electrocardiosignals and reducing dimensions of the depth features to obtain a majority of features and a minority of features;
the sample synthesis module is used for synthesizing the minority class of features based on the SMOTE algorithm to obtain a minority class of feature synthesis sample;
and the classification module is used for carrying out SVM classification on the original electrocardiosignals according to the majority class characteristics and the minority class characteristics to synthesize samples so as to obtain a classification result.
The method and the system have the beneficial effects that: according to the method, depth feature calculation based on a convolutional neural network architecture is combined with a SMOTE algorithm for generating a few types of samples, and the depth features capable of deeply mining ECG information are subjected to sample generation so as to improve the classification accuracy of the model.
Drawings
FIG. 1 is a flow chart of the steps of a method for classifying unbalanced single-lead electrocardiographic data according to the present invention;
FIG. 2 is a data processing diagram according to an embodiment of the present invention;
FIG. 3 is a block diagram of an unbalanced single lead classification system for ECG data;
FIG. 4 is a schematic exploded view of an ECG signal according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a convolutional neural network according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
Referring to fig. 1 and 2, the present invention provides an unbalanced single-lead electrocardiographic data classification method, which comprises the following steps:
acquiring an original electrocardiosignal and preprocessing the original electrocardiosignal to obtain a preprocessed electrocardiosignal;
carrying out depth feature extraction on the preprocessed electrocardiosignals and reducing dimensions of the depth features to obtain a majority class of features and a minority class of features;
synthesizing the minority class of features based on the SMOTE algorithm to obtain a minority class of feature synthesis sample;
and performing SVM classification on the original electrocardiosignals according to the majority class characteristics and the minority class characteristics to synthesize samples so as to obtain a classification result.
Specifically, the SVM is used for replacing a SoftMax classifier in the CNN, and the classification effect of the training model is improved.
As a preferred embodiment of the method, the step of obtaining the original electrocardiographic signal and preprocessing the original electrocardiographic signal to obtain a preprocessed electrocardiographic signal specifically includes:
acquiring an original electrocardiosignal;
carrying out six-layer decomposition on the original electrocardiosignals based on discrete wavelet transform to obtain high-frequency components and low-frequency components of corresponding layers;
specifically, the original electrocardiographic signal is decomposed in multiple layers by discrete wavelet transform, each layer includes a high-frequency component and a low-frequency component, and the high-frequency component and the low-frequency component of the next layer are decomposed from the low-frequency component of the previous layer, and the exploded view is shown in fig. 4.
Discarding the first layer high-frequency component, the second layer high-frequency component and the sixth layer low-frequency component, and reconstructing the remaining high-frequency component and low-frequency component to obtain a reconstructed electrocardiosignal;
and cutting and zero padding are carried out on the reconstructed electrocardiosignals, and the length of the reconstructed electrocardiosignal data is controlled to be 30s, so that the preprocessed electrocardiosignals are obtained.
Specifically, considering that the convolutional neural network requires equal length of input data of training data and prediction data, the ECG in the data set needs to be cut and zero-filled, the data with the time length longer than 30s is cut, only the last 30s data is retained, and zero-filling is performed on the data with the time length shorter than 30s, so that the data length is 30 s. After the clipping and zero padding operations, all data lengths are 30 s.
Further, as a preferred embodiment of the method, the step of performing depth feature extraction on the preprocessed electrocardiographic signals and performing dimension reduction on the depth features to obtain a majority of features and a minority of features specifically includes:
performing depth feature extraction on the preprocessed electrocardiosignals based on a pre-constructed convolutional neural network to obtain 64-dimensional features corresponding to a plurality of samples and a plurality of samples;
and performing dimensionality reduction processing on 64-dimensional features corresponding to the majority samples and the minority samples based on a PCA dimensionality reduction algorithm to obtain 41-dimensional majority features and minority features.
Further as a preferred embodiment of the method, the pre-constructed convolutional neural network comprises four convolutional layers, three max-pooling layers and three full-connected layers.
Specifically, a schematic diagram of the pre-constructed convolutional neural network structure is shown in fig. 5.
Further, as a preferred embodiment of the method, the step of synthesizing the minority class of features based on the SMOTE algorithm to obtain a minority class feature synthesis sample specifically includes:
for each 41-dimensional minority feature X, calculating the distance from X to all other features by taking the Euclidean distance as a standard to obtain corresponding k neighbors;
setting a sampling ratio and determining a sampling multiple N according to the unbalanced ratio of the majority class features and the minority class features;
for each minority class of features X, randomly selecting a plurality of features from the corresponding k neighbors according to the sampling multiple N, and selecting neighbor Xn;
and reconstructing the selected neighbor Xn to obtain a few class characteristic synthetic samples.
Further as a preferred embodiment of the method, the formula for reconstructing the sample for the selected neighbor Xn is as follows:
in the above formula, rand (0,1) represents randomly selecting an array from 0-1, X represents a few kinds of features,
representing the selected K neighbors.
As shown in fig. 3, an unbalanced single lead electrocardiographic data classification system comprises:
the geological disaster position map module is used for acquiring the positions of the hidden danger points of the geological disaster and drawing a geological disaster position map;
the preprocessing module is used for acquiring an original electrocardiosignal and preprocessing the original electrocardiosignal to obtain a preprocessed electrocardiosignal;
the feature extraction module is used for carrying out depth feature extraction on the preprocessed electrocardiosignals and reducing dimensions of the depth features to obtain a majority of features and a minority of features;
the sample synthesis module is used for synthesizing the minority class of features based on the SMOTE algorithm to obtain a minority class of feature synthesis sample;
and the classification module is used for carrying out SVM classification on the original electrocardiosignals according to the majority class characteristics and the minority class characteristics to synthesize samples so as to obtain a classification result.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. An unbalanced single-lead electrocardiogram data classification method is characterized by comprising the following steps:
acquiring an original electrocardiosignal and preprocessing the original electrocardiosignal to obtain a preprocessed electrocardiosignal;
carrying out depth feature extraction on the preprocessed electrocardiosignals and reducing dimensions of the depth features to obtain a majority class of features and a minority class of features;
synthesizing the minority class of features based on the SMOTE algorithm to obtain a minority class of feature synthesis sample;
and performing SVM classification on the original electrocardiosignals according to the majority class characteristics and the minority class characteristics to synthesize samples so as to obtain a classification result.
2. The method for classifying unbalanced single-lead electrocardiographic data according to claim 1, wherein the step of obtaining and pre-processing the original electrocardiographic signals to obtain pre-processed electrocardiographic signals specifically comprises:
acquiring an original electrocardiosignal;
carrying out six-layer decomposition on the original electrocardiosignals based on discrete wavelet transform to obtain high-frequency components and low-frequency components of corresponding layers;
discarding the first layer high-frequency component, the second layer high-frequency component and the sixth layer low-frequency component, and reconstructing the remaining high-frequency component and low-frequency component to obtain a reconstructed electrocardiosignal;
and cutting and zero padding are carried out on the reconstructed electrocardiosignals, and the length of the reconstructed electrocardiosignal data is controlled to be 30s, so that the preprocessed electrocardiosignals are obtained.
3. The method for classifying unbalanced single-lead electrocardiographic data according to claim 2, wherein the step of performing depth feature extraction on the preprocessed electrocardiographic signals and performing dimension reduction on the depth features to obtain a majority class feature and a minority class feature specifically comprises:
performing depth feature extraction on the preprocessed electrocardiosignals based on a pre-constructed convolutional neural network to obtain 64-dimensional features corresponding to a plurality of samples and a plurality of samples;
and performing dimensionality reduction processing on 64-dimensional features corresponding to the majority samples and the minority samples based on a PCA dimensionality reduction algorithm to obtain 41-dimensional majority features and minority features.
4. The method of classifying unbalanced single-lead electrocardiographic data according to claim 3 wherein the pre-constructed convolutional neural network comprises four convolutional layers, three max-pooling layers and three full-connected layers.
5. The method for classifying unbalanced single-lead electrocardiographic data according to claim 4, wherein the step of synthesizing the minority class features based on the SMOTE algorithm to obtain the minority class feature synthesis samples specifically comprises:
for each 41-dimensional minority feature X, calculating the distance from X to all other features by taking the Euclidean distance as a standard to obtain corresponding k neighbors;
setting a sampling ratio and determining a sampling multiple N according to the unbalanced ratio of the majority class features and the minority class features;
for each minority class of features X, randomly selecting a plurality of features from the corresponding k neighbors according to the sampling multiple N, and selecting neighbor Xn;
and reconstructing the selected neighbor Xn to obtain a few class characteristic synthetic samples.
6. The method of classifying unbalanced single lead electrocardiographic data according to claim 5 wherein the formula for reconstructing samples from the selected neighbor Xn is as follows:
in the above formula, rand (0,1) represents randomly selecting an array from 0-1, X represents a few kinds of features,
representing the selected K neighbors.
7. An unbalanced single lead electrocardiographic data classification system comprising:
the preprocessing module is used for acquiring an original electrocardiosignal and preprocessing the original electrocardiosignal to obtain a preprocessed electrocardiosignal;
the feature extraction module is used for carrying out depth feature extraction on the preprocessed electrocardiosignals and reducing dimensions of the depth features to obtain a majority of features and a minority of features;
the sample synthesis module is used for synthesizing the minority class of features based on the SMOTE algorithm to obtain a minority class of feature synthesis sample;
and the classification module is used for carrying out SVM classification on the original electrocardiosignals according to the majority class characteristics and the minority class characteristics to synthesize samples so as to obtain a classification result.
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