Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling

Fetal arrhythmias are a life-threatening disorder occurring in up to 2% of pregnancies. If identified, many fetal arrhythmias can be effectively treated using anti-arrhythmic therapies. In this paper, we present a novel method of detecting fetal arrhythmias in short length non-invasive fetal electrocardiography (NI-FECG) recordings. Our method consists of extracting a fetal heart rate time series from each NI-FECG recording and computing an entropy profile using a data-driven range of the entropy tolerance parameter . To validate our approach, we apply our entropy profiling method to a large clinical data set of 318 NI-FECG recordings. We demonstrate that our method ( ) provides strong performance for classifying arrhythmic fetuses (AUC of 0.83) and outperforms entropy measures such as (AUC of 0.68) and (AUC of 0.72). We also find that NI-FECG recordings incorrectly classified using the investigated entropy measures have significantly lower signal quality, and that excluding recordings of low signal quality (13.5% of recordings) increases the classification performance of (AUC of 0.90). The superior performance of our approach enables automated detection of fetal arrhythmias and warrants further investigation in a prospective clinical trial.