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Zaylaa, A. ; Dax, J.* ; Sippel, K. ; Semeia, L. ; Frohlich ; Gallard, A.* ; Wallois, F.* ; Eswaran, H.* ; Birkenfeld, A.L. ; Preissl, H.

Enhancing fetal brain imaging: ALPS-FMEG technique achieves accurate signal extraction by mitigating movement artifacts.

Ann. Biomed. Eng., DOI: 10.1007/s10439-026-03977-2 (2026)
Publ. Version/Full Text DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
PURPOSE: Fetal magnetoencephalography (fMEG) enables non-invasive monitoring of fetal brain function with high temporal resolution. However, how can we isolate low signal-to-noise ratio signals of the developing brain when disruptive artifacts arise from maternal and fetal movements? Addressing this challenge is critical for understanding brain development. We present Advanced Localization and Processing of fMEG Signals based on Maternal and Gross fetal body Movement Exclusion (ALPS-FMEG), a MATLAB-based framework that improves fetal brain signals by removing fetal and maternal movement artifacts. METHODS: ALPS-FMEG integrates Independent Component Analysis for separation and reconstruction of fetal brain, fetal and maternal cardiac signal components in sensor space, Empirical Mode Decomposition for noise reduction, and a movement artifact detection-and-exclusion technique based on actogramCOG associated with heart rate patterns. This novel integration modifies the actogramCOG approach by pre-interpolating R waves for enhanced robustness and combines it with HRV-based logic gates, representing a first in fMEG processing to achieve artifact-free signals while preserving physiological latencies. RESULTS: ALPS-FMEG was applied to 50 fMEG datasets from 28 to 39 weeks of gestation, enhancing signal quality. For group analysis, 45 datasets were retained after excluding recordings with auditory event-related field (fAEF) latencies < 70 ms. In these, it significantly improved signal-to-noise ratio and fAEF amplitudes (p < 0.0001), with preserved latencies. fAEF latency showed a significant negative correlation with gestational age (p < 0.001). CONCLUSION: ALPS-FMEG improves fetal brain signal extraction by addressing movement artifacts. This method supports robust fetal brain analysis and may be adaptable to future fMEG systems, including optically pumped magnetometers, enhancing prenatal neurophysiology and clinical research, though manual steps currently limit scalability and could be addressed via automation for broader practical use.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Empirical Mode Decomposition (emd) ; Fetal Auditory Event-related Fields (faefs) ; Fetal Magnetoencephalogram (fmeg) ; Fetal Neurodevelopment ; Gross Fetal Movement Detection ; Independent Component Analysis (ica); Magnetoencephalography; Algorithm; Fields; Recordings; Potentials; Fetuses
ISSN (print) / ISBN 0090-6964
e-ISSN 0191-5649
Journal Annals of Biomedical Engineering
Publisher Springer
Publishing Place One New York Plaza, Suite 4600, New York, Ny, United States
Reviewing status Peer reviewed
Institute(s) Institute of Diabetes Research and Metabolic Diseases (IDM)
Grants Hermann von Helmholtz-Gemeinschaft Deutscher Forschungszentren e.V. IVF (EXNET.01-17.)
Deutsche Forschungsgemeinschaft
Agence Nationale de la Recherche
German Federal Ministry of Education and Research (BMBF) to the German Centre for Diabetes Research
Helmholtz Zentrum Mnchen - Deutsches Forschungszentrum fr Gesundheit und Umwelt (GmbH) (4209)