Sampling rate impacts the quality of HRV estimates. In the context of the recent research article by Burma et al published in JBI which systematically examined this matter, I discuss this notion more deeply with practical implications to biomedical informatics. Not all HRV metrics are created equal regarding their sensitivity to sampling rate errors when their health predictive performance is concerned. A combination of several, especially nonlinear HRV metrics can remedy these sampling rate constraints. I present methodology for comprehensive validation of the effect of sampling rate on HRV.