Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain.
Servicio de Obstetricia, H.U.P. La Fe, 46026 Valencia, Spain.
Sensors (Basel). 2021 Sep 10;21(18):6071. doi: 10.3390/s21186071.
One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.
科学界面临的挑战之一是预测早产,为此,电子宫描记术(EHG)已成为一种高度敏感的预测技术。样本熵和模糊熵已被用于EHG 信号的特征描述,但它们需要优化许多内部参数。气泡熵和散布熵都被提出用于生物医学信号的特征描述,其中气泡熵仅需要一个内部参数,而散布熵可以检测频率和幅度的任何变化。在这项工作中,我们试图通过分析其作为单个特征的判别能力以及通过使用遗传算法选择特征的妇产科数据、线性和非线性 EHG 特征以及线性判别分析来开发六个预测模型,来确定这些熵测度在预测早产方面的临床价值。与样本熵、频谱熵和模糊熵相比,气泡熵和散布熵能够更好地区分早产和足月组。熵测度为线性特征提供了补充信息,实际上,包含其他非线性特征对模型性能的提高可以忽略不计。在测试数据集时,获得的最佳模型性能的 F1 得分为 90.1±2%。这个模型可以很容易地适应实时应用,从而有助于 EHG 技术在临床实践中的可转移性。