Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography

Sensors

Gema Prats-Boluda¹  Julio Pastor-Tronch¹  Javier Garcia-Casado¹  Yiyao Ye-Lin¹  Alfredo Perales Marín²  Vicente Diago²  Rogelio Monfort-Ortíz²  Alba Roca Prats² 

[1] Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain;Servicio de Obstetricia, Hospital Universitario y Politécnico de La Fe, 46026 Valencia, Spain;

关键词: electrohysterogram;    uterine myoelectrical activity;    tocolytic therapy;    random forest;    extreme learning machine;    K-nearest neighbors;   

DOI  :  10.3390/s21072496

来源: DOAJ

【 摘 要 】

Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th–90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RF_{F1_2} and ELM_{F1_2} provided the highest F1-score values in the validation dataset, (88.17 ± 8.34% and 90.2 ± 4.43%) with the 50th percentile of EHG and obstetric inputs. ELM_{F1_2} outperformed RF_{F1_2} in sensitivity, being similar to those of ELM_Sens (sensitivity optimization). The 10th–90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability.

【 授权许可】

Unknown