Research On Intelligent Decision Method Of Cardiovascular Diseases Based On Multi-Lead ECG Signal

Nonlinear time-varying signals are a type of multi-component signals whose frequency and amplitude change with time.The combination process characteristics of multiple signals in a multivariable system often show a high degree of complexity.Its classification processing was signal analysis and artificial intelligence research.An important subject of research in the field.ECG electrocardiogram signal is extremely vulnerable to interference.It is a weak signal with non-stationarity.Each cardiac cycle period is composed of a series of regular waveforms.These waveforms are recorded separately and the detailed information of heart activity status is an important analysis basis for the diagnosis of heart disease.Most of the current research is based on the time-varying signal as a short time,stable and linear,and ignores the information carried by the signal over time,and in actual research,due to some case events Factors such as less occurrence and expensive sample sampling bring many difficulties to the modeling and analysis of the signal system.How to use small sample data to study and process cardiovascular diseases,and at the same time,improve data processing on the basis of ensuring diagnostic accuracy Speed becomes the key research content of this article.The specific research contents are as follows:1.ECG signals are susceptible to noise interference caused by the environment.For different types of high and low frequency noise frequency distribution differences,Butterworth low-pass filtering is used to achieve noise filtering of ECG signals.2.To deal with the problem of data imbalance,use signal resampling technology to process the ECG signal data set,normalize the data in the data set,and give the processing method.3.Aiming at the problem of modeling and classification of small sample sets of time-varying signals,a deep multi-scale radial basis process neural network(DLMS-RBFPNN)is proposed.The model consists of a time-varying signal input layer,a multi-scale radial basis kernel transformation layer,a fully connected layer,and a perceptron classifier.Taking into account the spectral characteristics of time-varying signals and the diversity of distribution patterns,based on radial basis process neural network,by linearly superimposing Gauss kernel functions with different width parameters,a multi-scale kernel is formed to complete the process signal morphological characteristics at different scales Extraction,identification and similarity measurement.By superimposing the fully connected layer and the classifier on the radial basis kernel function layer,the fusion and classification of time-varying signals with different scale features are realized.4.Aiming at the problem of lack of fast and effective fault classification methods in the diagnosis of medical diseases,an integrated classification model analysis of ECG lead grouping algorithm based on decision tree is proposed.The simple structure of the model avoids the cumbersome parameter training process.At the same time,Maximizing the probability that the sample data is correctly classified as a learning goal enables the model to focus more on the category information of the data,reduce the interference of irrelevant feature information,improve the accuracy of the classification results,and verify it through experiments.