Automatic filter selection for Hilbert autocorrelation and cyclic repetition frequency based fPCG analysis

Background and Objectives Fetal phonocardiography (fPCG) is a passive and non-invasive technique for measuring fetal heart rate. Due to the presence of external and internal noises, filtering is mandatory to obtain clinically usable signals from fPCG recordings. Frequency characteristics of the fPCG signal recorded from the mother's abdomen change with fetal parameters like gestation week, fetal position, etc. This makes the choice of filter frequencies challenging and unproductive. Therefore, our aim is to develop automatic filter selection based on fetal heart sounds characteristics. Methods This work presents algorithms for the automatic filter selection based on fPCG signal characteristics. The automatic filter selection algorithms are coupled with fetal heart rate estimation algorithms based on Hilbert-autocorrelation and cyclic repetition frequency. Three performance measures are determined to test the algorithms: accuracy, reliability, and success rate. Results The automatic filter selection show significant improvement in success rate when compared with manually selected fixed frequency band filters. The success rate improves from 79% to 93% for Hilbert-autocorrelation based algorithm and 71% to 90% for cyclic repetition frequency-based algorithm. Conclusion Automated filter selection could enable robust automated estimation of fetal heart rate from fPCG recordings across variety of clinical settings.