Lullaby: A Novel Algorithm to Extract Fetal QRS in Real Time Using Periodic Trend Feature

Area of Science:

• Biomedical Engineering
• Cardiology
• Perinatal Medicine

Background:

• Fetal heart rate (fHR) monitoring is crucial for assessing fetal well-being.
• Current ultrasound methods are intermittent, require expert operation, and are clinic-based.
• Wearable technology offers potential for continuous home-based fHR monitoring using maternal abdominal ECG (aECG).

Purpose of the Study:

• To develop and validate a novel, efficient algorithm for fetal QRS complex extraction from aECG.
• To address limitations of existing methods in terms of speed and computational load for wearable applications.

Main Methods:

• Development of the Lullaby algorithm for fetal QRS extraction from aECG signals.
• Validation of the algorithm's performance against existing methods.

Main Results:

• The Lullaby algorithm demonstrated a significant speed improvement, being nearly 7 times faster than current methods.
• Achieved a superior F1-score of 0.815, indicating enhanced accuracy in fetal QRS extraction.
• The algorithm is computationally efficient, suitable for real-time processing in wearable devices.

Conclusions:

• The Lullaby algorithm offers a promising solution for real-time, home-based fetal heart rate monitoring.
• This advancement has the potential to revolutionize perinatal monitoring by enabling continuous and accessible assessment of fetal cardiac health.