Multi domain fusion feature extraction and classification of ECG based on PCA-ICA

With the rapid development of social economy and information technology, human physiological characteristics such as fingerprints, face, palm print, iris, retina, etc. have been widely used in the field of commercial biometrics. In recent years, the dynamic physiological characteristics of human body, such as ECG, heart sound and voice, have been proved to be applicable to biometrics. This paper mainly studies the feature extraction and classification of ECG signals. First, the ECG signal is periodically segmented to obtain the time-domain feature matrix, and the periodic signal is wavelet-transformed to obtain the frequency-domain feature matrix. Then PCA-ICA is used to perform latitude reduction on the feature matrix. Finally, the parameters of the fuzzy decision tree for modeling are intelligently set by the PSO algorithm. And experimental verification on the MIT-BIH standard ECG database.