Key result
The Ensemble Kalman Filter algorithm achieved an average F1-score of 97.25% for fetal QRS detection, outperforming the Extended Kalman Filter which achieved 88.90%.
Why the study?
Extracting fetal ECG from abdominal ECG is challenging due to high noise, especially when using only a single-channel signal in compact devices.
Does the Ensemble Kalman Filter algorithm improve fetal ECG extraction accuracy from single-channel abdominal ECG compared to the Extended Kalman Filter?
Does the Ensemble Kalman Filter algorithm improve fetal ECG extraction accuracy from single-channel abdominal ECG compared to the Extended Kalman Filter?
Absolute Event Rate: 97.25% vs 88.9%
The proposed Ensemble Kalman Filter algorithm effectively extracts fetal ECG from single-channel maternal abdominal signals, outperforming the Extended Kalman Filter even in the presence of motion noise.
EnKF may advance single-channel fetal ECG extraction; leaves open whether gains improve perinatal outcomes in prospective studies.
Fetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i . e . in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman Filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed Extended Kalman Filter (EKF) based algorithm.
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Sarafan et al. (2021) studied Fetal ECG monitoring (n=85). Ensemble Kalman Filter (EnKF) algorithm vs. Extended Kalman Filter (EKF) algorithm was evaluated on Average F1-score for fetal QRS detection on PhysioNet 2013 Challenge bank. The Ensemble Kalman Filter algorithm achieved an average F1-score of 97.25% for fetal QRS detection, outperforming the Extended Kalman Filter which achieved 88.90%.
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