SVM electrocardiosignal recognition method based on improved grey wolf algorithm optimization
A technology of electrocardiographic signal and recognition method, which is applied in pattern recognition, character and pattern recognition, calculation, etc. in the signal, which can solve the problem of low recognition accuracy and achieve low myoelectric interference, high accuracy, and fast convergence speed Effect
Active Publication Date: 2021-01-01
NANCHANG UNIV
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Problems solved by technology
However, the current electrocardiogram recognition only serves as an auxiliary diagnosis, and the final diagnosis still requires experts to make a decision; the existing automatic electrocardiogram signal recognition system still has problems such as low recognition accuracy, so the recognition of electrocardiogram signals still remains to be done. There is still a lot of room for research
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[0044] Attached below Figure 1~2 The technical scheme of the present invention is further explained.
[0045] The present invention provides a SVM ECG signal recognition method optimized based on the improved gray wolf algorithm, the flow chart is as follows figure 1 As shown, the specific implementation steps are as follows:
[0046] Step S1: collecting ECG data;
[0047] Step S2: After simple denoising processing on the ECG signal, feature extraction is performed by wavelet transform, thereby obtaining the entropy feature of the original ECG signal sample and wavelet energy ratio features And this composition fusion feature space is used as the input vector of the classification model;
[0048] Step S3: Enter the improved gray wolf algorithm (DIGWO), initialize the position of α, β, δ wolves and the objective function value of the wolf pack, the position of each gray wolf individual is composed of the penalty factor C to be optimized and the kernel function parameter ...
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The invention discloses an SVM electrocardiosignal recognition method based on improved grey wolf algorithm optimization, and the improved grey wolf algorithm (DIGWO) is used for optimizing parametersof a support vector machine (SVM) so as to improve the electrocardiosignal recognition rate of the SVM. According to the method, collected ECG signals are subjected to multi-scale sample entropy andwavelet energy ratio feature extraction to be fused into a feature space to serve as an input vector, a dynamic double-subgroup strategy is introduced on the basis of a traditional grey wolf algorithm, improvement is conducted from the three aspects of subgroup division, convergence factors and proportion weight, and then the improved grey wolf algorithm is used for seeking the optimal combinationof the penalty factor C and the kernel function parameter g of the SVM, and a DIGWOSVM classification model is trained and constructed. The electrocardiosignal recognition method provided by the invention improves the electrocardiosignal recognition efficiency and the accuracy of an analysis result.
Description
technical field [0001] The invention relates to the field of electrocardiographic signal identification, in particular to an SVM electrocardiographic signal identification method optimized based on an improved gray wolf algorithm. Background technique [0002] According to the World Health Organization (WHO), the number of people who die from non-communicable diseases in the world accounts for two-thirds of the global death toll every year. Among them, the incidence of cardiovascular disease ranks first. Around the world, about 17.5 million people die from cardiovascular-related diseases every year. In recent years, the prevalence of cardiovascular disease in my country has also continued to rise. The number of people suffering from cardiovascular disease has reached 290 million, which also makes the mortality rate of this type of disease much higher than that of other diseases. More than two, it becomes the disease that poses the greatest threat to people's health. Theref...
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Login to View More IPC IPC(8): G06K9/00G06K9/62G06N3/00
CPCG06N3/006G06F2218/04G06F2218/08G06F18/2411G06F18/253
Inventor 刘继忠李继发徐文斌
Owner NANCHANG UNIV