Based on the Matlab platform, this paper studies the wavelet feature extraction method of ECG signal and applies it to the simulation experiment of ECG signal disease identification. First, the basic characteristics of ECG signals and common ECG diseases are introduced. Then, the principle and method of wavelet transform are expounded in detail, and a feature extraction algorithm of ECG signal based on wavelet decomposition and wavelet reconstruction is proposed. Finally, a set of simulation experiments for ECG disease identification is designed, and the effectiveness of the extracted wavelet features in ECG disease identification is verified by analyzing the experimental data and displaying the results.
Article directory
1 Introduction
ECG signal is an important bioelectrical signal, which is of great significance to the diagnosis and monitoring of heart diseases. Traditional ECG signal analysis methods mainly rely on frequency domain and time domain features, but these methods cannot fully extract the local features of the signal. As a time-frequency analysis method, wavelet transform can extract local features of signals in time domain and frequency domain simultaneously, so it is widely used in ECG signal processing.
2. Basic characteristics and common diseases of ECG
The ECG signal is a signal formed by the current generated by the electrical activity of the heart, which contains a wealth of information. Common ECG signal features include heart rate, QRS waveform, ST segment and T wave, etc. Cardiac electrical diseases mainly include arrhythmia, myocardial ischemia and myocardial infarction.
3. Principle and method of wavelet transform
Wavelet transform is a method of decomposing signals into different frequency components, which decomposes and reconstructs signals by selecting different wavelet basis functions. Commonly used wavelet basis functions include Daubechies wavelet and Haar wavelet. Wavelet transform has the characteristics of multi-scale analysis, and can extract the time-frequency characteristics of signals.
4. ECG signal processing algorithm based on wavelet feature extraction
This paper proposes a feature extraction algorithm of ECG signal based on wavelet decomposition and wavelet reconstruction. Firstly, the ECG signal is decomposed by wavelet, and the detail coefficients and approximate coefficients of different frequency components are obtained. Then, according to the characteristics of the ECG signal, a suitable wavelet basis function is selected to extract the features of the detail coefficients. Finally, the extracted features are combined by wavelet reconstruction to obtain the final feature vector.
5. Simulation experiment design of ECG signal disease identification
In order to verify the effectiveness of the extracted wavelet features in ECG disease identification, a set of ECG signal disease identification simulation experiments was designed. First, a dataset of ECG signals containing different ECG disorders is collected. Then, the data set is divided into training set and test set, the training set is used to train the classification model, and the test set is used for model evaluation. Finally, through the analysis and display of the experimental results, the accuracy and effectiveness of the extracted wavelet features in the identification of ECG diseases are verified.
6. Complete source code + data set download
ECG signal wavelet feature extraction and corresponding disease identification simulation based on Matlab (source code + data set).rar: https://download.csdn.net/download/m0_62143653/88189922
