Multiscale entropy profiling to estimate complexity of heart rate dynamics

In the analysis of signal regularity from a physiological system such as the human heart, Approximate entropy (HA) and Sample entropy (HS) have been the most popular statistical tools used so far. While studying heart rate dynamics, it nevertheless becomes more important to extract information about complexities associated with the heart, rather than the regularity of signal patterns produced by it. A complex physiological system does not necessarily produce irregular signals and vice versa. In order to equip a regularity statistic to see through the respective system's level of complexity, the idea of multiscaling was introduced in HS estimation. Multiscaling ideally requires an input signal to be (a) long and (b) stationary. However, the longer the data is the less stationary it is. The requirement multiscaling places on its data length largely limits its accuracy. We propose a novel method of entropy profiling that makes multiscaling require very short signal segments, granting better prospects of signal stationarity and estimation accuracy. With entropy profiling, an efficient multiscale HS based analysis requires only 500-beat signals of atrial fibrillated data, as opposed to the earlier case that required at least 20 000 beats.