Why the study?
It is unclear which wrist-worn photoplethysmography-derived heart rate variability and morphological features are valuable for tracking maternal autonomic regulation and health.
Do morphological and HRV features derived from wrist-worn photoplethysmography differentiate healthy pregnant women from non-pregnant women?
Do morphological and HRV features derived from wrist-worn photoplethysmography differentiate healthy pregnant women from non-pregnant women?
Morphological features from wrist-worn PPG, particularly systolic pulse wave deterioration, are superior to HRV features in differentiating healthy pregnant women from non-pregnant women, suggesting potential utility in tracking maternal health.
May inform PPG feature selection for maternal monitoring; leaves open validation for complication prediction in prospective studies.
Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.
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Bester et al. (2023) studied this question.
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