Key result
A novel clustering-based method for single-channel fetal heart rate monitoring achieved an F1-measure of 98.63% for fetal QRS detection, demonstrating high accuracy compared to reference annotations.
A novel clustering-based algorithm improves the accuracy of non-invasive fetal heart rate extraction from single-channel abdominal ECGs compared to traditional threshold-based methods.
May support single-channel fetal monitoring; hypothesis-generating and requires prospective validation before clinical adoption.
Non-invasive fetal electrocardiography (ECG) is based on the acquisition of signals from abdominal surface electrodes. The composite abdominal signal consists of the maternal electrocardiogram along with the fetal electrocardiogram and other electrical interferences. These recordings allow for the acquisition of valuable and reliable information that helps ensure fetal well-being during pregnancy. This paper introduces a procedure for fetal heart rate extraction from a single-channel abdominal ECG signal. The procedure is composed of three main stages: a method based on wavelet for signal denoising, a new clustering-based methodology for detecting fetal QRS complexes, and a final stage to correct false positives and false negatives. The novelty of the procedure thus relies on using clustering techniques to classify singularities from the abdominal ECG into three types: maternal QRS complexes, fetal QRS complexes, and noise. The amplitude and time distance of all the local maxima followed by a local minimum were selected as features for the clustering classification. A wide set of real abdominal ECG recordings from two different databases, providing a large range of different characteristics, was used to illustrate the efficiency of the proposed method. The accuracy achieved shows that the proposed technique exhibits a competitve performance when compared to other recent works in the literature and a better performance over threshold-based techniques.
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Castillo et al. (2018) studied Fetal heart rate monitoring. Clustering-based method (k-medoids++) for fetal QRS detection vs. Reference annotations (Direct fetal ECG) was evaluated on F1-measure for fetal QRS detection (best performing channel). A novel clustering-based method for single-channel fetal heart rate monitoring achieved an F1-measure of 98.63% for fetal QRS detection, demonstrating high accuracy compared to reference annotations.
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