Key result
The proposed SA-GWO algorithm achieved an average accuracy of 94.60% for the Labour dataset and 95.66% for the Pregnancy dataset in detecting fetal QRS complexes from non-invasive abdominal ECGs.
Why the study?
In non-invasive fetal ECG monitoring, maternal signals overlap in time and frequency domains and dominate in magnitude, making fetal ECG extraction challenging.
The proposed GWO-SA algorithm demonstrates high accuracy in extracting non-invasive fetal ECG signals from maternal abdominal recordings, potentially improving fetal heart rate monitoring.
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May aid non-invasive fetal monitoring; leaves open prospective clinical validation before practice change.
This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal Electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women’s body, especially maternal electro-cardiogram (mECG). These signals are more distinct in magnitude and overlap in time and frequency domains, making the fECG extraction extremely challenging. The proposed extraction method combines the Grey wolf algorithm (GWO) with Sequential Analysis (SA). This innovative combination, forming the GWO-SA method, optimises the parameters required to create a template that matches the mECG, which leads to an accurate elimination of the said signal from the input composite signal. The extraction system was tested on two databases consisting of real signals, namely, Labour and Pregnancy. The results show that the proposed method extracts the fetal ECG signal with an outstanding efficacy. The efficacy of the results was evaluated based on accurate detection of the fQRS complexes. The parameters used to evaluate are as follows: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. Due to the stochastic nature of the GWO algorithm, ten individual runs were performed for each record in the two databases to assure stability as well as repeatability. Using these parameters, for the Labour dataset, we achieved an average ACC of 94.60%, F1 of 96.82%, SE of 97.49%, and PPV of 98.96%. For the Pregnancy database, we achieved an average ACC of 95.66%, F1 of 97.44%, SE of 98.07%, and PPV of 97.44%.
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Raj et al. (2022) studied Fetal monitoring (n=22). Sequential Analysis with Grey Wolf Optimiser (SA-GWO) was evaluated on Accuracy (ACC) of fQRS complex detection. The proposed SA-GWO algorithm achieved an average accuracy of 94.60% for the Labour dataset and 95.66% for the Pregnancy dataset in detecting fetal QRS complexes from non-invasive abdominal ECGs.
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