In a pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical data with high accuracy in noisy real-life environments, but little is known about the utility of DL in non-invasive biometrics during pregnancy. We hypothesized that a recently established self-supervised learning (SSL) approach that provides emotional recognition from ECG will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG. Chronically stressed mothers and controls matched on enrollment at 32 weeks of gestation were studied. We validated the chronic stress exposure by psychological inventory, maternal hair cortisol, and FSI. We tested two variants of SSL architecture, one trained on the generic ECG features for emotional recognition obtained from public datasets and another transfer-learned on a subset of our data. Our DL models predict excellently the chronic stress exposure  , the individual psychological score , and FSI values at 34 weeks of gestation and maternal cortisol at birth  . The best performance was achieved with the DL model trained on the public dataset and using maternal ECG alone. The present DL approach provides a novel source of physiological insights into complex multi-modal relationships between different regulatory systems exposed to chronic stress. The developed DL model can be deployed in low-cost regular ECG biosensors as a simple ubiquitous early stress detection and exposure tool during pregnancy. This discovery should enable early behavioral interventions.
In a pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical data with high accuracy in noisy real-life environments, but little is known about the utility of DL in non-invasive biometrics during pregnancy. We hypothesized that a recently established self-supervised learning (SSL) approach that provides emotional recognition from ECG will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG. Chronically stressed mothers and controls matched on enrolment at 32 weeks of gestation were studied. We validated the chronic stress exposure by psychological inventory, maternal hair cortisol, and FSI. We tested two variants of SSL architecture, one trained on the generic ECG features for emotional recognition obtained from public datasets and another transfer-learned on a subset of our data. Our DL models predict excellently the chronic stress exposure group (AUROC 0.982+/-0.002) , the individual psychological stress score (R2 0.943+/-0.009) , and FSI at 34 weeks of gestation (R2 0.946+/-0.013) as well as the maternal hair cortisol at birth reflecting chronic stress exposure (R2 0.931+/-0.006) . The best performance was achieved with the DL model trained on the public dataset and using maternal ECG alone. The present DL approach provides a novel source of physiological insights into complex multi-modal relationships between different regulatory systems exposed to chronic stress. The developed DL model can be deployed in low-cost regular ECG biosensors as a simple ubiquitous early stress detection and exposure tool during pregnancy. This discovery should enable early behavioral interventions.
R
enrollment
enrolment
A
group (AUROC 0.982+/-0.002)
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score
stress score (R2 0.943+/-0.009)
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values
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and maternal
(R2 0.946+/-0.013) as well as the maternal hair
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reflecting chronic stress exposure (R2 0.931+/-0.006)