Key result
The proposed FastICA and SVD algorithm achieved a signal-to-noise ratio of 45.028 dB and a fetal QRS wave detection sensitivity of 96.90%, PPV of 98.23%, and F1 score of 95.24%.
Why the study?
Fetal electrocardiogram signals are extremely weak and greatly influenced by noise, leaving FECG signal extraction and detection challenging.
The proposed combination of FastICA and SVD algorithms effectively extracts fetal ECG signals and accurately detects QRS waves, offering a potential new computational method for fetal monitoring.
Promising for fetal ECG signal processing research; leaves open prospective clinical validation before practice adoption.
Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.
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Hao et al. (2022) studied Fetal electrocardiogram signal extraction. FastICA combined with SVD algorithm was evaluated on Fetal QRS wave detection (sensitivity, positive predictive value, F1 score) and signal-to-noise ratio. The proposed FastICA and SVD algorithm achieved a signal-to-noise ratio of 45.028 dB and a fetal QRS wave detection sensitivity of 96.90%, PPV of 98.23%, and F1 score of 95.24%.
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