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Although Zika virus (ZIKV) seems to be prominently neurotropic, there are some reports of involvement of other organs, particularly the heart. Of special concern are those children exposed prenatally to ZIKV and born with no microcephaly or other congenital anomaly. Electrocardiogram (ECG) - derived heart rate variability (HRV) metrics represent an attractive, low cost, widely deployable tool for early identification of such children. We hypothesized that HRV in such children would yield a biomarker of fetal ZIKV exposure. We investigated the HRV properties of 21 infants aged 4 to 25 months from Brazil. The infants were divided in two groups, the ZIKV-exposed (n=13) and controls (n=8). Single channel ECG was recorded in each child at ~15 months of age and HRV was analyzed in 5 min segments to provide a comprehensive characterization of the degree of variability and complexity of the heart rate. Using a cubic Support Vector Machine (SVM) classifier we identified babies as Zika cases or controls with negative predictive value of 92% and positive predictive value of 86%. Our results show that HRV metrics can help differentiate between ZIKV-affected, yet asymptomatic, and non-ZIKV exposed babies. We identified the Grid Count as the best HRV measure in this study allowing such differentiation, regardless the presence of microcephaly. We show that it is feasible to measure HRV in infants and toddlers using a small non-invasive portable ECG device and that such approach may uncover memory of in utero exposure to ZIKV. This approach may be useful for future studies and low-cost screening tools involving this challenging to examine population.