Detection of transient episodes in heart rate variability signals

This paper compares the performance of four approaches for the detection of transient episodes in the heart rate variability (HRV) records. These are based on autoregressive (AR) modeling, discrete wavelet transforms (DWT), wavelet packet transforms, and hidden Markov modeling (HMM). A competitive neural network has been applied for classification and the results of the four techniques have been compared. It has been concluded that the autoregressive model is the most efficient technique for detecting the essential features describing the transient episodes in HRV.