Key result
Non-linear and scale-invariant analyses of heart rate variability, such as the multi-scaling Zipf's law for low-variability periods, provide valuable diagnostic information beyond traditional linear measures.
Non-linear and scale-invariant analyses of heart rate variability provide deeper insights into autonomic regulation than traditional linear measures, though clinical application remains challenged by artifacts from daily activities.
Non-linear HRV analyses may enhance diagnostics; leaves open prospective validation before clinical adoption.
Human heart rate fluctuates in a complex and non-stationary manner. Elaborating efficient and adequate tools for the analysis of such signals has been a great challenge for the researchers during last decades. Here, an overview of the main research results in this field is given. The following question are addressed: (a) what are the intrinsic features of the heart rate variability signal; (b) what are the most promising non-linear measures, bearing in mind clinical diagnostic and prognostic applications.
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Kalda et al. (2003) conducted a review in Heart Rate Variability. Non-linear and scale-invariant analysis vs. Linear measures was evaluated. Non-linear and scale-invariant analyses of heart rate variability, such as the multi-scaling Zipf's law for low-variability periods, provide valuable diagnostic information beyond traditional linear measures.
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