Objective: Preterm birth is a large-scale clinical problem involving over 10% of infants. Diagnostic means for timely risk assessment are lacking and the underlying physiological mechanisms unclear. To improve the evaluation of pregnancy before term, we introduce dedicated entropy measures derived from a single-channel electrohysterogram (EHG). Methods: The estimation of approximate entropy (ApEn) and sample entropy (SampEn) is adjusted to monitor variations in the regularity of single-channel EHG recordings, reflecting myoelectrical changes due to pregnancy progression. In particular, modifications in the tolerance metrics are introduced for improving robustness to EHG amplitude fluctuations. An extensive database of 58 EHG recordings with 4 monopolar channels in women presenting with preterm contractions was manually annotated and used for validation. The methods were tested for their ability to recognize the onset of labor and the risk of preterm birth. Comparison with the best single-channel methods according to the literature was performed. Results: The reference methods were outperformed. SampEn and ApEn produced the best prediction of delivery, although only one channel showed a significant difference (p <0.04) between labor and nonlabor. The modified ApEn produced the best prediction of preterm delivery, showing statistical significance (p <0.02) in three channels. These results were also confirmed by the area under the receiver operating characteristic curve and fivefold cross validation. Conclusion: The use of dedicated entropy estimators improves the diagnostic value of EHG analysis earlier in pregnancy. Significance: Our results suggest that changes in the EHG might manifest early in pregnancy, providing relevant prognostic opportunities for pregnancy monitoring by a practical single-channel solution.
|Number of pages||10|
|Journal||IEEE Transactions on Biomedical Engineering|
|Publication status||Published - Apr 2018|
- preterm delivery
- Premature Birth/diagnosis
- Uterine Monitoring/methods
- Signal Processing, Computer-Assisted
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M.B. (Beatrijs) van der Hout-van der Jagt (Content manager) & Eugenie Delvaux (Content manager)
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