Fetal QRS detection and heart rate estimation: a wavelet-based approach

Area of Science:

• Biomedical Engineering
• Signal Processing
• Obstetrics

Background:

• Fetal heart rate monitoring is crucial for pregnancy surveillance but faces technical limitations.
• Existing methods struggle to accurately detect fetal QRS complexes amidst maternal signals.
• Advances in signal analysis are needed to improve perinatal outcomes.

Purpose of the Study:

• To adapt a wavelet transform-based QRS detector for accurate fetal QRS detection from abdominal fetal ECG.
• To develop a robust method for fetal heart rate (FHR) estimation by excluding maternal ECG interference.
• To validate the proposed method against established fetal ECG databases.

Main Methods:

• Adapted a previously published wavelet transform QRS detector for fetal physiology.
• Implemented a single lead (SL) detector and combined it with post-processing rules (SLR) for fetal QRS detection.
• Validated the SLR method using PhysioNet data with reference fetal QRS locations, comparing it to SL and ICA-based detections.

Main Results:

• The SLR method demonstrated superior performance compared to SL and ICA-based detections.
• Over 80% of processed files showed an estimated FHR error below 20 bpm.
• The median error in 1-minute FHR estimation was 0.13 bpm, with a reference FHR correlation of 0.48 (increasing to 0.73 for FHR > 110 bpm).

Conclusions:

• The proposed methodology provides a clinically useful estimation of FHR.
• The adapted wavelet transform detector effectively isolates fetal QRS signals.
• This technique holds promise for improving fetal surveillance and perinatal care.