Cardiac arrhythmia is a serious disorder in heart electrical activity that may have fatal consequences especially if not detected early. This motivated the development of automated arrhythmia detection systems that can early detect and accurately recognize arrhythmias thus significantly improving the chances of patient survival. In this paper, we propose an improved arrhythmia detection system particularly designed to identify five different types based on nonlinear dynamical modeling of electrocardiogram signals. The new approach introduces a novel distance series domain derived from the reconstructed phase space as a transform space for the signals that is explored using classical features. The performance measures showed that the proposed system outperforms state of the art methods as it achieved 98.7% accuracy, 99.54% sensitivity, 99.42% specificity, 98.19% positive predictive value, and 99.85% negative predictive value.