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In the context of fetal heart monitoring (prenatal and during childbirth), the SurFAO project offers an alternative to current clinical routines. The challenge is to extract, from non-invasive sensors on the maternal abdomen, a fetal electrocardiogram (ECGf) of great quality allowing a clinical diagnosis (follow-up of the FHR (Fetal Heart Rate)) and extraction of ECG waveforms).
The approach proposes a technological breakthrough shared by a consortium of researchers and clinicians. The originality is driven by innovative methodological choices: the use of a multimodal system (ECG coupling with PCG (phonocardiography)) for the signal acquisition in order to increase the robustness of information extraction, by taking into account clinical uses and the need to support the monitoring process, and by setting up a multimodal database.
The objective is to feed a database that will be used in the future to develop ECGf extraction methods.
Full description
To monitor the well-being of a fetus or for clinical diagnosis, the challenge is to extract a high-quality fetal electrocardiogram (fECG) signal from non-invasive sensors on the maternal abdomen.
As early as the 20th week of amenorrhea, heart rhythm disorders (tachycardia, bradycardia) can be detected in the fetus, most often by fortuitous circumstance, during routine obstetrical ultrasound examinations. It is then necessary to analyze these rhythmic anomalies, understand their origin and, if necessary, initiate pharmacotherapy. The effectiveness of the treatments is then monitored by ultrasound in the high-risk pregnancy department.
The analysis of a fetal electrocardiogram (fECG) provides information that allows to determine the nature of the rhythm disorder, its origin and therefore its potential severity.
The innovative methodological approach considered for the extraction of non-invasive ECGf is to combine 2 complementary modalities of the same cardiac phenomenon. This is achieved by combining the use of ECG sensors with sound sensors giving access to phonocardiographic signals (PCG).
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20 participants in 1 patient group
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Matthias LACHAUD, MD; Isabelle Boudry, PhD
Data sourced from clinicaltrials.gov
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