Time-frequency or time-variant methods have been extensively applied in the study of the heart-rate variability (HRV) signal. In fact, the frequency content of HRV signal has a strong correlation with the control system assessing heart rate. In particular, the power related to the low-frequency (LF) and high-frequency (HF) components have been demonstrated to correlate to the action of sympathetic and parasympathetic branches of the autonomic nervous system. However, the analysis is restricted to stationary conditions, unless time-frequency methods are employed for detecting dynamic changes that may occur during physiological and pathological conditions. This article reviews the most diffused tools for time-frequency analysis, starting from linear decomposition of the signal (including short-time Fourier transform and wavelet and wavelet packet decomposition), to quadratic time-frequency distributions (including Wigner-Ville transform and Cohen's class of distributions), and finally to adaptive or time-variant autoregressive (AR) models, in both the mono- and bivariate forms. In the past few years, these approaches have been applied in several studies related to cardiovascular responses during nonstationary pathophysiological events. Among them, we will recall and discuss myocardial ischemia (spontaneous or induced), drug infusion, rest-tilt maneuver and syncope, neurophysiological, and sleep investigations.