Wavelet transform to quantify heart rate variability and to assess its instantaneous changes

Heart rate variability is a recognized parameter for assessing autonomous n ervous system activity. Fourier transform, the most commonly used method to analyze variability, does not offer an easy assessment of its dynamics bec ause of limitations inherent in its stationary hypothesis. Conversely, wave let transform allows analysis of nonstationary signals. We compared the res pective yields of Fourier and wavelet transforms in analyzing heart rate va riability during dynamic changes in autonomous nervous system balance induc ed by atropine and propranolol. Fourier and wavelet transforms were applied to sequences of heart rate intervals in six subjects receiving increasing doses of atropine and propranolol. At the lowest doses of atropine administ ered, heart rate variability increased, followed by a progressive decrease with higher doses. With the first dose of propranolol, there was a signific ant increase in heart rate variability, which progressively disappeared aft er the last dose. Wavelet transform gave significantly better quantitative analysis of heart rate variability than did Fourier transform during autono mous nervous system adaptations induced by both agents and provided novel t emporally localized information.