Nonlinear baseline estimation of FHR signal using empirical mode decomposition

Automated analysis of fetal heart rate (FHR) curve plays a significant role in computer-aided fetal monitoring. The first and critical step is the estimation of the FHR baseline. A number of FHR baseline estimation algorithms have been developed but recent studies have pointed out the deficiency of such algorithms when dealing with non-stationary and non-linear FHR signals of continuous decelerations especially in intrapartum tracings. Our study proposes a novel non-linear FHR baseline estimation method using empirical mode decomposition (EMD) and a statistical post-processing method. To assess the baseline quality, we made a comparative study against a cited linear baseline estimation algorithm using auto-regressive moving average (ARMA) model-based simulated FHR signals of continuous acceleration and deceleration patterns. The results were evaluated in terms of basal FHR values, detected acceleration and deceleration numbers versus preset basal FHR values and acceleration/deceleration numbers. The results showed that when dealing with non-stationary and non-linear FHR tracings like intra-partum tracings with continuous accelerations or decelerations, the EMD-based baseline estimation method is more stable and interference-resistant than the traditional linear methods.