In this paper, an algorithm based on a joint use of spectral and nonlinear techniques for heart rate variability (HRV) analysis is proposed. First, the measured RR data are passed into a trimmed moving average (TMA)-based filtering system to generate a lower frequency (LF) time series and a higher frequency (HF) one that approximately reflect the sympathetic and vagal activities, respectively. Since the Lyapunov exponent can be used to characterize the level of chaos in complex physiological systems, the largest Lyapunov exponents corresponding to the complex sympathetic and vagal systems are then estimated from the LF and HF time series, respectively, using an existing algorithm. Numerical results of a postural maneuver experiment indicate that both characteristic exponents or their combinations might serve as a set of innovative and robust indicators for HRV analysis, even under the contamination of sparse impulses due to aberrant beats in the RR data.