Electrocardiogram (ECG) analysis is one of the most important approaches to cardiac arrhythmia detection. In this paper, we propose an ECG analysis approach with adaptive feature selection and support vector machines (SVMs). Many wavelet transform-based coefficients are used as candidates, but only a few coefficients are selected for classification problem of each class pair. In addition, the several variation classes are partitioned into two or more subclasses to improve the training efficiency of SVMs. The experimental results show that the proposed ECG analysis approach can obtain high recognition rate and reliable results.