Key result
A detrending method based on ensemble empirical mode decomposition achieved better performance for heart rate variability analysis than the smoothing priors approach.
EEMD provides a superior method for detrending HRV signals compared to the widely used SPA method, potentially improving the accuracy of HRV analysis.
May improve HRV detrending accuracy in research; leaves open clinical validation and adoption.
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.
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Zeng et al. (2014) studied Heart rate variability analysis. Ensemble empirical mode decomposition (EEMD) vs. Smoothing priors approach (SPA) was evaluated on Detrending performance. A detrending method based on ensemble empirical mode decomposition achieved better performance for heart rate variability analysis than the smoothing priors approach.
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