# Non-invasive abdominal fetal electroencephalography for fetal assessment > **NIH NIH R21** · YALE UNIVERSITY · 2024 · $451,850 ## Abstract ABSTRACT Every year in the United States, over 35,000 newborns suffer from neurologic injury at the time of birth due to hypoxia. Despite widespread use of cardiac external fetal monitoring (EFM) for the last 60 years, the rate of fetal neurologic injury has not decreased, highlighting the urgent need for better assessment methods in labor. Currently, clinicians still rely on cardiac EFM, which reflects a downstream effect of initial neurologic damage due to interruptions in fetal oxygenation resulting in subsequent heart rate changes. Relying on downstream effects can be inaccurate and result in both false positive and false negative diagnoses. Evaluating fetal brain activity via fetal electroencephalography (fEEG) has been shown to be a promising method as it shows abrupt signal changes up to ten minutes before cardiac EFM changes measured in fetuses with acidemia. These ten minutes in delivering a fetus can be the difference between lifelong cerebral palsy and a developmentally normal child. However, vaginal fetal EEG (V-fEEG), the only currently available method, is invasive, requiring vaginal electrodes attached to the fetal scalp. Consequently, it has been abandoned as a monitoring method. Non- computerized abdominal fetal EEG, which is non-invasive, has been proposed, but is uninterpretable due to extensive signal artifact from non-EEG signals from the abdomen due to maternal and fetal movement, muscle activity, and maternal and fetal electrocardiogram signal. Our work overcomes this challenge by harnessing advances in artificial intelligence to identify and filter out non-fetal EEG signals, allowing fetal neurologic activity to be accurately and non-invasively measured. We have developed an algorithm that has been refined and applied to patients that shows classical fetal EEG response to auditory stimuli, or evoked brain stem potentials. Using this algorithm, fetal EEG signals can be separated cleanly from maternal and other fetal noise. Our hypothesis is that our method of computerized abdominal fetal EEG (cAb-fEEG) can rapidly and accurately reconstruct fetal neurologic activity and is equivalent to invasive V-fEEG monitoring. To test this hypothesis, we will first compare our method of cAb-fEEG to direct V-fEEG in a group of 46 patients to quantify reconstruction error (regression residuals) between the signals. Second, neurology clinical experts in EEG interpretation will evaluate the neurological features of both cAb-fEEG and V-fEEG. To achieve these aims, we have assembled and will lead a team of experts in EEG research, electrical and computational engineering, clinical neurology, obstetrics, and pediatrics. We expect the results of this study will formally provide a strong base for cAb-fEEG and lay the foundation for future clinical studies to evaluate cAb-fEEG as a monitoring method to improve perinatal outcomes. The results of this research will provide a non-invasive and novel method to directly measure fetal neurologic activit... ## Key facts - **NIH application ID:** 10989190 - **Project number:** 1R21HD115285-01A1 - **Recipient organization:** YALE UNIVERSITY - **Principal Investigator:** Jose A CORTES-BRIONES - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $451,850 - **Award type:** 1 - **Project period:** 2024-09-15 → 2026-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10989190 ## Citation > US National Institutes of Health, RePORTER application 10989190, Non-invasive abdominal fetal electroencephalography for fetal assessment (1R21HD115285-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10989190. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*