Study of myocardial infraction feature extraction using subjective introduction based method

Most of the existing studies on myocardial infraction (MI) feature extraction are based on certain important components. The subjective significance based method was introduced into the feature extraction from an entire ECG segment for the classification in the paper. The method was employed to discriminate the assumed prior class from the other classes and separate each of the classes at the same time. The data in the analysis including healthy control (HC), myocardial infraction in early stage (MIES) and acute myocardial infraction (AMI) was collected from PTB diagnostic ECG database which is the latest public database for various research purposes. The results show that the proposed method can obtain the effective features from the ECGs with 12-leads for the classification purpose.