A Novel Approach to Arrhythmia Classification Using Rr Interval and Teager Energy

It is hypothesized that a key characteristic of electrocardiogram (ECG) signal is its nonlinear dynamic behaviour and that the nonlinear component changes more significantly between normal and arrhythmia conditions than the linear component. The usual statistical descriptors used in RR (R to R) interval analysis do not capture the nonlinear disposition of RR interval variability. In this paper we explore a novel approach to extract the features from nonlinear component of the RR interval signal using Teager energy operator (TEO). The key feature of Teager energy is that it models the energy of the source that generated the signal rather than the energy of the signal itself. Hence any deviations in regular rhythmic activity of the heart get reflected in the Teager energy function. The classification evaluated on MIT-BIH database, with RR interval and mean of Teager energy computed over RR interval as features, exhibits an average accuracy that exceeds 99.79%.