A dilated inception CNN-LSTM network for fetal heart rate estimation

OBJECTIVE: Fetal heart rate monitoring is routinely used during pregnancy and labor to assess fetal well-being. The non-invasive 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 heart rate 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 heart rate 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 heart rate. 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 heart rate 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 heart rate extraction in clinical practice.