摘要：Heart rate variability (HRV) is a key indicator for assessing autonomous nervous system activity. Because nonstationary and slow trends which can cause distortion to HRV analysis are usually occurred in HRV signals, detrending scheme is necessary before HRV analysis. Ensemble empirical mode decomposition (EEMD), which offers the ability to break down signals into a set of intrinsic mode functions and acts as a high-pass filter through partial reconstruction, is proposed for HRV detrending. Experiment results show that the detrending method based on EEMD can achieve better performance than the smoothing priors approach (SPA), which is one of the most widely used method.
