Fetal Twin: a mechanistic computational model of fetal physiology for heart-rate-variability biomarker research
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Fetal-monitoring biomarkers for neonatal hypoxic-ischemic brain injury face a structural gap: the mechanistic ground truth that would label a training set — perfusion pressure, the moment of decompensation, the injury time course — cannot be measured at scale or ethically in human pregnancy or labor, and generative synthetic data carry no mechanistic labels. We address this with a mechanistic computational model of the fetal cardiovascular, autonomic, and metabolic response to controlled hypoxic stress, and use it to test how beat detection and acquisition fidelity alter the interpretation of fetal-heart-rate-variability (HRV) biomarkers. The model integrates these systems forward in time across antepartum development (gestational-age growth scaling) and intrapartum stress (umbilical-cord occlusions), emitting synthetic monitoring signals (fetal heart rate, RR intervals) co-registered with model-computed latent labels (pH, base deficit, lactate, perfusion pressure, decompensation and injury states). In a fetal-sheep-derived autonomic-loop configuration we report three results. First, a phase-accumulator beat detector shows that the apparently physiologic baseline HRV of an earlier build was largely a detector artifact, and a noise-off control shows beat-to-beat variability requires an explicit stochastic driver rather than self-sustained autonomic oscillation. Second, a sampling-fidelity sweep yields a fidelity-matched selection rule: a deceleration-area biomarker is preserved at CTG-grade 4 Hz whereas RMSSD is corrupted there (inflated about 8-fold by timing quantization) and recovers only at fetal-ECG rates. Third, autonomic modulation alone does not reproduce the published RMSSD rise-then-collapse — a negative result that motivates, but does not prove, an intrinsic sinoatrial-pacemaker contribution as a testable hypothesis. This is an in-silico, hypothesis-generating study: the model is not validated for individual fetal prediction, clinical risk estimation, or clinical decision-making. The model is implemented as Fetal Twin (engine fetaltwin ), a source-available research instrument released under a noncommercial license, together with all figure configurations, so that these controlled experiments are reproducible.
Key Points
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Progress on fetal-monitoring biomarkers for neonatal brain-injury risk is constrained by a structural gap: the mechanistic ground truth that would label a training set — perfusion pressure, the moment of cardiovascular decompensation, the time course of injury — cannot be measured at scale or ethically during human pregnancy or labor.
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We present Fetal Twin (source-available engine fetaltwin ), a publicly available, noncommercially-licensed mechanistic testbed for fetal physiological development. It integrates the fetal cardiovascular, metabolic, and autonomic systems forward in time across antepartum development (gestational-age growth scaling) and intrapartum stress (umbilical-cord occlusions), and emits synthetic monitoring signals (fetal heart rate, RR intervals) co-registered with model-computed latent labels (pH, lactate, perfusion pressure, decompensation and injury states).
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The name’s digital-twin connotation is deliberate but bounded: Fetal Twin is a mechanistic, population-level twin of fetal physiology used as a research instrument — not a validated, patient-specific clinical digital twin or predictor. Its purpose is to interrogate what candidate biomarkers can and cannot mean, via in-silico controls impossible in vivo — swapping the beat detector, turning a noise source off, ablating a reflex, or quantizing the signal to a monitor’s sampling grid.
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Demonstrations in a fetal-sheep-derived autonomic-loop configuration show that beat-to-beat HRV amplitude can be a numerical artifact of the beat detector, and that in this model class beat-to-beat variability requires an explicit stochastic driver — it does not arise as a self-sustained oscillation of the deterministic autonomic loop.
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A further demonstration establishes a fidelity-matched biomarker-selection rule — a deceleration-area biomarker survives CTG-grade 4 Hz sampling whereas RMSSD is corrupted at that rate and needs fetal-ECG timing — and a negative result shows the published RMSSD “rise-then-collapse” is not reproducible from autonomic modulation in this model class, motivating (but not proving) an intrinsic-pacemaker hypothesis.
