Quantitative Non-Stationary Assessment of Cerebral Hemodynamics by Empirical Mode Decomposition of Cerebral Doppler Flow Velocity

Dynamic regulation of cerebral circulation involves complex interaction between cardiovascular, respiratory, and autonomic nervous systems. Evaluating cerebral hemodynamics by using traditional statistic- and linear-based methods would underestimate or miss important information. Complementary ensemble empirical mode decomposition (CEEMD) has great capability of adaptive feature extraction from non-linear and non-stationary data without distortion. This study applied CEEMD for assessment of cerebral hemodynamics in response to physiologic challenges including paced 6-cycle breathing, hyperventilation, 7% CO2 breathing and head-up tilting test in twelve healthy subjects. Intrinsic mode functions (IMFs) were extracted from arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) signals, and was quantified by logarithmic averaged period and logarithmic energy density. The IMFs were able to show characteristics of ABP and CBFV waveform morphology in beat-to-beat timescale and in long-term trend scale. The changes in averaged period and energy density derived from IMFs were helpful for qualitative and quantitative assessment of ABP and CBFV responses to physiologic challenges. CEEMD is a promising method for assessing non-stationary components of systemic and cerebral hemodynamics.