Enhancing Fetal Brain Imaging: ALPS-FMEG Technique Achieves Accurate Signal Extraction by Mitigating Movement...

Area of Science:

• Neuroscience
• Biophysics
• Medical Imaging

Background:

• Fetal magnetoencephalography (fMEG) offers high temporal resolution for monitoring fetal brain function.
• Movement artifacts from maternal and fetal activity significantly degrade signal quality in fMEG.
• Isolating low signal-to-noise ratio fetal brain signals is crucial for understanding neurodevelopment.

Purpose of the Study:

• To present ALPS-FMEG, a MATLAB-based framework designed to remove maternal and fetal movement artifacts.
• To enhance the extraction of fetal brain signals from noisy fMEG data.
• To improve the reliability of fMEG for studying fetal brain development.

Main Methods:

• ALPS-FMEG integrates Independent Component Analysis (ICA) for signal separation and Empirical Mode Decomposition (EMD) for noise reduction.
• A novel movement artifact detection and exclusion technique combines actogramCOG with heart rate variability (HRV)-based logic gates.
• The method incorporates pre-interpolation of R waves for enhanced robustness in artifact detection.

Main Results:

• ALPS-FMEG was applied to 50 fMEG datasets (28-39 weeks gestation), improving overall signal quality.
• After artifact exclusion, 45 datasets showed significantly improved signal-to-noise ratio and fetal auditory event-related field (fAEF) amplitudes (p < 0.0001).
• fAEF latencies were preserved and showed a significant negative correlation with gestational age (p < 0.001).

Conclusions:

• ALPS-FMEG effectively improves fetal brain signal extraction by mitigating movement artifacts.
• The framework supports robust fetal brain analysis and has potential adaptability to future fMEG systems.
• Automation of current manual steps is recommended to enhance scalability for broader clinical research applications.