Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling. (28th February 2022)

• Record Type: Journal Article
• Authors: Keenan, Emerson; Karmakar, Chandan; Udhayakumar, Radhagayathri K; Brownfoot, Fiona C; Lakhno, Igor; Shulgin, Vyacheslav; Behar, Joachim A; Palaniswami, Marimuthu
• Is Part Of: Physiological measurement. Volume 43:Number 2(2022)
• Publication Date: 2022-02-28
• Subjects: arrhythmia -- entropy -- fetal ECG -- fetal monitoring -- signal quality; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/ac4e6d
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objective. 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. Approach. 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 r . To validate our approach, we apply our entropy profiling method to a large clinical data set of 318 NI-FECG recordings. Main Results. We demonstrate that our method ( TotalSampEn ) provides strong performance for classifying arrhythmic fetuses (AUC of 0.83) and outperforms entropy measures such as SampEn (AUC of 0.68) and FuzzyEn (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 TotalSampEn (AUC of 0.90). Significance. The superior performance of our approach enables automated detection of fetal arrhythmias and warrants further investigation in a prospective clinical trial.