TY - JOUR
T1 - Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling
AU - Keenan, Emerson
AU - Karmakar, Chandan
AU - Udhayakumar, Radhagayathri K.
AU - Brownfoot, Fiona C.
AU - Lakhno, Igor
AU - Shulgin, Vyacheslav
AU - Behar, Joachim A.
AU - Palaniswami, Marimuthu
N1 - Publisher Copyright:
© 2022 Institute of Physics and Engineering in Medicine.
PY - 2022/2/28
Y1 - 2022/2/28
N2 - 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.
AB - 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.
KW - arrhythmia
KW - entropy
KW - fetal ECG
KW - fetal monitoring
KW - signal quality
UR - http://www.scopus.com/inward/record.url?scp=85127590120&partnerID=8YFLogxK
U2 - 10.1088/1361-6579/ac4e6d
DO - 10.1088/1361-6579/ac4e6d
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AN - SCOPUS:85127590120
SN - 0967-3334
VL - 43
JO - Physiological Measurement
JF - Physiological Measurement
IS - 2
M1 - 025008
ER -