Cardiac variability time-series analysis by sample entropy and multiscale entropy

Abstract

Ever since the use of heart rate variability (HRV) as a tool for quantification of autonomic nervous system dynamics has become popular, the interest in exploring the hidden complexities of physiologic time series has increased immensely. The sample entropy (SampEn) has been in use for quite some time as a complexity measure. In the present work, the SampEn was evaluated for certain physiological and pathological time series. It is demonstrated that SampEn is small for higher values of tolerance r and is able to distinguish different physiologic time series with tolerance level r = 0.2 to 0.3 for data lengths of approximately three thousand RR interval samples. It was further observed that this traditional algorithm exhibits higher complexity for pathologic subjects than for healthy physiologic subjects, which is misleading observation. This may be due to the fact that SampEn do not account for multiple time scales inherent in the physiologic time series. The above considered physiologic and pathologic time series were also evaluated with multiscale entropy (MSE). The time–series were observed to be more effectively distinguished for patients with congestive heart failure (CHF), sudden cardiac death (SCD), atrial fibrillation (AF) and partial epilepsy.