[[alternative]]lectrocardiogram Analysis with Adaptive Feature Selection and Support Vector Machines

[[abstract]]Electrocardiogram signal (ECG) provides the functional aspects of the heart and cardiovascular system. In order to monitor the real-time evolution of the patients, the ECG signal is sometimes recorded continuously for one or more days. The availability of more and more information on the physical status and evolution of the patient is always desirable, but usually the information needs to be assimilated and evaluated by doctors or nurses. We propose a new wavelet transform based ECG analysis algorithm with improving the feature extraction and classifier design. Inherited from the properties of WT, the extracted vectors can represent the most important features for ECG signals. It is particularly true for the QRS complex the can be recognized as the high frequency and high energy components. The system adopts support vector machines (SVM) to differentiate the types of heart diseases.