Open AccessA dilated inception CNN-LSTM network for fetal heart rate estimation
Author(s) -
Eleni Fotiadou,
Ruud J. G. van Sloun,
Judith O. E. H. van Laar,
Rik Vullings
Publication year - 2021
Publication title -
physiological measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.674
H-Index - 101
eISSN - 1361-6579
pISSN - 0967-3334
DOI - 10.1088/1361-6579/abf7db
Subject(s) - convolutional neural network , fetus , fetal heart rate , robustness (evolution) , computer science , subtraction , artificial intelligence , deep learning , pattern recognition (psychology) , heart rate , cardiology , medicine , pregnancy , blood pressure , mathematics , biochemistry , genetics , chemistry , arithmetic , gene , biology
Objective . Fetal heart rate (HR) monitoring is routinely used during pregnancy and labor to assess fetal well-being. The noninvasive fetal electrocardiogram (ECG), obtained by electrodes on the maternal abdomen, is a promising alternative to standard fetal monitoring. Subtraction of the maternal ECG from the abdominal measurements results in fetal ECG signals, in which the fetal HR can be determined typically through R-peak detection. However, the low signal-to-noise ratio and the nonstationary nature of the fetal ECG make R-peak detection a challenging task. Approach . We propose an alternative approach that instead of performing R-peak detection employs deep learning to directly determine the fetal HR from the extracted fetal ECG signals. We introduce a combination of dilated inception convolutional neural networks (CNN) with long short-term memory networks to capture both short-term and long-term temporal dynamics of the fetal HR. The robustness of the method is reinforced by a separate CNN-based classifier that estimates the reliability of the outcome. Main results . Our method achieved a positive percent agreement (within 10% of the actual fetal HR value) of 97.3% on a dataset recorded during labor and 99.6% on set-A of the 2013 Physionet/Computing in Cardiology Challenge exceeding top-performing state-of-the-art algorithms from the literature. Significance . The proposed method can potentially improve the accuracy and robustness of fetal HR extraction in clinical practice.