Abstract
Non-Gaussian fluctuations are observed in a wide variety of biomedical time series. To characterize such nonGaussian time series, we introduce a multiplicative stochastic process in which an observed time series is assumed to be described by the multiplication of Gaussian and amplitude random variables. In this framework, we propose an analysis method using log-amplitude cumulants, which is capable of characterizing a wide class of symmetric unimodal distributions with fat tails. As an application of this method, we will discuss non-Gaussian property of heart rate variability.
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