Research On Simulation And Identification Of ECG Signal Based On 256 Leads

The incidence of cardiovascular disease remains high in my country,especially with the improvement of people’s living standards,more and more people are troubled by cardiovascular disease.At present,due to the high prevalence of cardiovascular disease and the risk of the disease,even today with the continuous development of science and technology,the disease still ranks first in the fatality rate.Therefore,it is urgent to prevent and treat cardiovascular disease.In the diagnosis of cardiovascular disease,the electrocardiogram is indispensable.Through the electrocardiogram,doctors can make a general judgment on the patient’s condition,so the electrocardiogram is of great significance in clinical practice.However,due to the diversification of diseases and the specificity of some ECG signals,it may be difficult for doctors to judge the ECG,so it is particularly important to seek methods to assist doctors in diagnosis.In recent years,with the continuous progress of deep learning technology,it has repeatedly achieved good results in image recognition tasks.Therefore,it is of great significance and promising application prospects to integrate deep learning into the auxiliary diagnosis of ECG signals.256 multi-lead ECG signal is one of the research hotspots in recent years,because it can measure more detailed electrical potential changes on the surface of the human body,pay attention to more subtle features,better reflect the human heart information,and help doctors determine whether it is healthy or not.The specific disease,so this topic mainly focuses on the identification and research of 256-lead ECG data.However,due to the lack of 256-lead ECG data at present,there is no relevant and complete database,in order to avoid the problem of over-fitting of samples during the experiment,which makes it impossible to identify abnormal samples more accurately,it is necessary to generate 256-lead ECG signals.for the purpose of amplifying the sample.Based on this,the experiment of the subject is mainly divided into two parts: on the one hand,the 256-lead ECG data is generated by the generation algorithm of the time series data;on the other hand,the classification algorithm identifies the ECG signal.The main research contents of this paper are as follows:(1)In view of the problem of insufficient samples of multi-lead ECG data,based on the particularity of ECG data as a time series signal,two generation algorithms are mainly proposed and used,namely CNN-BiLSTM model and 2DUnetVPTransformer model.By extracting the spatial and temporal features of the ECG signal separately,it is better to generate the ECG signal with the same distribution.At the same time,the second generation algorithm is not limited to generating ECG signals with a single disease.Through multi-channel fusion of time-space attention mechanism,it can generate ECG signals containing multiple diseases at the same time,which is innovative in thinking.Through the spatiotemporal attention mechanism,the autoregressive inference speed of the model is improved,and the accumulated inference error can also be reduced.Since this method requires data with specific disease labels,and the existing 256-lead ECG data has no relevant labels,the method is mainly tested on the 12-lead dataset.In addition,SARIMA,Wave Net,and Time GAN models are also used as baseline models in the experiment,and the comparison and analysis with the baseline model results show the effectiveness of the proposed algorithm.(2)For the problem of similarity evaluation of time series data,the CoFlux algorithm is mainly used.The algorithm pays attention to the flux characteristics of the sequence,and judges its flux correlation existence,time sequence and fluctuation direction.The three indicators are progressive relationships,which directly reflect the similarity measure between the generated ECG data and the original data.(3)For the classification and identification of ECG signals,the model based on the attention residual network is mainly used.By deepening the number of network layers,the model can recognize more abstract features,and the use of skip connections in the network can also alleviate the problem of gradient disappearance caused by the deepening of network layers.Two attention mechanisms,channel attention and spatial attention,are used in the model to make it better focus on temporal features.Experiments on different datasets can not only reflect the performance of the classification model,but also indirectly support the evaluation and discussion of the two generation algorithms,indicating the applicability of the model.The experimental results and the comparative analysis with the baseline show that the two ECG signal generation models proposed and applied in this paper,CNNBiLSTM and 2DUnet-VPTransformer,have outstanding performance.Classification recognition of labels.The classification algorithm based on the attention residual network can also improve the recognition accuracy,showing unique advantages in the ECG data set,and has a good application prospect in the future.