The goal of this study was to determine a set of timing, shape, and statistical features available through noninvasive monitoring of maternal electrocardiogram and photoplethysmography that identifies preeclamptic patients.METHODS:
Pregnant women admitted to Labor and Delivery were monitored with pulse oximetry and electrocardiogram for 30 minutes. Photoplethysmogram features and heart rate variability were extracted from each data set and applied to a sequential feature selection algorithm to discriminate women with preeclampsia with severe features, from normotensive and hypertensive controls. The classification boundary was chosen to minimize the expected misclassification cost. The prior probabilities of the misclassification costs were assumed to be equal.RESULTS:
Thirty-seven patients with clinically diagnosed preeclampsia with severe features were compared with 43 normotensive controls; all were in early labor or beginning induction. Six variables were used in the final model. The area under the receiver operating characteristic curve was 0.907 (standard error [SE] = 0.004) (sensitivity 78.2% [SE = 0.3%], specificity 89.9% [SE = 0.1%]) with a positive predictive value of 0.883 (SE = 0.001). Twenty-eight subjects with chronic or gestational hypertension were compared with the same preeclampsia group, generating a model with 5 features with an area under the curve of 0.795 (SE = 0.007; sensitivity 79.0% [SE = 0.2%], specificity 68.7% [SE = 0.4%]), and a positive predictive value of 0.799 (SE = 0.002).CONCLUSIONS:
Vascular parameters, as assessed noninvasively by photoplethysmography and heart rate variability, may have a role in screening women suspected of having preeclampsia, particularly in areas with limited resources.