Heart rate variability (HRV) quantifies the fluctuations of the lengths of consecutive heart beat intervals, and is a reliable descriptor of many physiological factors modulating the normal rhythm of the heart. As the heart rate signal is nonstationary, indicators deduced from it may be present at all times, but may also occur episodically at nonpredetermined time instances. The potential for real-time feedback long-term ambulatory recordings is thus apparent. Numerous methods for measuring HRV have been standardized and are in active use, but are typically not designed to operate at real time. In this paper, we study the most popular HRV quantification methods and propose streaming algorithms that maximally utilize previously computed information without altering the output of the methods. We demonstrate speedups of more than two orders of magnitude for typical use-case scenarios. Using our algorithms on embedded systems that compute HRV leads to dramatic decreases in power consumption and in some cases allows for computation of metrics that were not previously possible at real time.