Key result
The ICA-FTF-CEEMDAN hybrid algorithm effectively extracted fetal ECGs, achieving 92.98% accuracy for fQRS detection on the FECGDARHA database and 78.47% on the Challenge 2013 database.
Why the study?
To identify the most suitable combination of adaptive and non-adaptive methods for extracting non-invasive fetal electrocardiograms from maternal abdominal signals, as FTF and CEEMDAN had not been extensively tested as a hybrid method.
Does the ICA-FTF-CEEMDAN hybrid algorithm accurately extract non-invasive fetal electrocardiograms from maternal abdominal recordings?
Does the ICA-FTF-CEEMDAN hybrid algorithm accurately extract non-invasive fetal electrocardiograms from maternal abdominal recordings?
The ICA-FTF-CEEMDAN hybrid algorithm effectively extracts non-invasive fetal ECGs from maternal abdominal recordings with high accuracy.
Hypothesis-generating for non-invasive fetal ECG extraction; prospective validation required before clinical adoption.
This study aimed to find the most suitable combination of adaptive and non-adaptive methods for extraction of non-invasive fetal electrocardiogram (NI-fECG) using signals recorded from the mother’s abdomen. Among the nine methods considered, the combination of independent component analysis (ICA), fast transversal filter (FTF), and complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) proved to be the most effective for the extraction of fECG from abdominal recordings. This combined method was suitable due to both being effective in extracting fECG and being less computationally complex. Further, so far, FTF and CEEMDAN methods have not been extensively tested for fECG extraction, and in particular, have not been examined as a hybrid method. The ICA-FTF-CEEMDAN hybrid algorithm was tested on two patient databases: Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations (FECGDARHA) and PhysioNet Challenge 2013. The evaluation of the accuracy of fQRS complexes detection was performed using the following parameters: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. The fetal heart rate (fHR) determination accuracy was evaluated using Bland–Altman plots and fHR traces. When testing on the FECGDARHA database, average values of ACC =92.98%, SE =95.33%, PPV =96.4% and F1 =95.86% for detection fQRS were achieved. The error in estimating the fHR was −1.02±7.02 (μ±1.96σ) bpm. When testing on the Challenge 2013 database, average values of ACC =78.47%, SE =82.06%, PPV =87.90% and F1 =84.62% for fQRS detection were achieved, and the error in estimating the fHR was −6.62±10.33 (μ±1.96σ) bpm. In addition, a non-invasive morphological analysis (ST analysis) was performed on the records from the FECGDARHA database, which was accurate in 7 of 12 records with values of μ<0.03 and values of ±1.96σ<0.04.
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Barnova et al. (2021) studied Fetal electrocardiogram extraction. ICA-FTF-CEEMDAN hybrid algorithm was evaluated on fQRS complexes detection accuracy and fetal heart rate determination. The ICA-FTF-CEEMDAN hybrid algorithm effectively extracted fetal ECGs, achieving 92.98% accuracy for fQRS detection on the FECGDARHA database and 78.47% on the Challenge 2013 database.
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