Automated fetal heart rate analysis for baseline determination using EMAU-Net

Automatic baseline determination is crucial for reducing the occurrence of fetal acidosis in clinical practice. However, there is a nonnegligible gap between the results of automatic baseline determination and the consensus of experts. In this paper, we propose a novel deep learning approach for baseline determination. First, potential accelerations/decelerations are recognized from the fetal heart rate and excluded by an ensemble multiattention U-Net. Then, the reference baseline and reliable interval are calculated via long- and short-term filters. Based on the filters, unreliable points for estimating the baseline are removed, and the final baseline is determined. We evaluate the performance of the proposed method on a public and a private database. Compared with state-of-the-art methods, our method yields better performance (the root mean square difference between baselines (BL. RMSD), F-measures for acceleration and deceleration (Acc/Dec. F-measures), the synthetic inconsistency coefficient (SI), and the morphological analysis discordance index (MADI) are 2.84 bpm, 0.80, 0.77, 48.9% and 3.94%, respectively) on the public database. The proposed method performs optimally in all metrics on the private database (BL. RMSD, Acc/Dec. F-measures, SI, and MADI are 1.75 bpm, 0.88, 0.80, 43.5%, and 3.11%, respectively). The experimental results indicate the effectiveness and generalizability of the proposed method.