Research On ECG Signal Feature Classification Based On Fuzzy Decision Tree

Heart disease is a common type of cardiovascular disease that has serious risks to human health,and by using an electrocardiogram,the health of the heart can be effectively observed.Therefore,it is of great significance to study ECG signals,and this paper proposes a classification method for ECG signal characteristics of fuzzy decision trees,which is used to study the classification of ECG signals and realize the automatic classification function of different types of ECG signals.First,the design of the acquisition system for ECG signals.STM32F103 is used as the main control board,ADS1292 R is the front-end acquisition core,and finally 65 groups of ECG signals are collected.Secondly,the preprocessing method of ECG signal is studied.Common ECG interference signals include EMG interference,power frequency interference and baseline drift,on the basis of mastering the principle of wavelet transform and ECG data sources,the preprocessing of ECG signals is realized through multi-level decomposition of ECG signals and wavelet base and selection of decomposition layers and thresholds.Experiments show that the wavelet threshold method has a good denoising effect,which lays a good foundation for the next step of feature extraction.Then,the feature extraction of ECG signals is studied.The ECG signal fused with time-domain features and frequency-domain features is used as the initial feature matrix of the classification model,the initial time-domain features are obtained by preprocessing the periodic signals,the initial frequency-domain features are extracted by wavelet transform,and the feature extraction link is completed by fusing the time-frequency features and then dimensionality reduction.The decision tree algorithm is compared with other classification algorithms to verify the effectiveness of feature extraction.Finally,the automatic classification method of ECG signal is studied,and the feature classification method based on fuzzy decision tree is used to optimize the ECG signal.Through the research of common classification algorithms,the advantages of decision tree classification algorithm in ECG classification are compared and analyzed.In order to overcome the shortcomings of decision tree classification algorithm and improve the classification accuracy,fuzzy theory is integrated into decision tree classification algorithm to form fuzzy decision tree.The fuzzy decision tree is optimized by gentic algorithm to further improve the classification effect and accuracy.