Department of Multimedia, Laboratory for Biomedical Computer Systems and Imaging, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Department of Obstetrics and Gynecology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
PLoS One. 2024 Sep 12;19(9):e0308797. doi: 10.1371/journal.pone.0308797. eCollection 2024.
The current trends in the development of methods for non-invasive prediction of premature birth based on the electromyogram of the uterus, i.e., electrohysterogram (EHG), suggest an ever-increasing use of large number of features, complex models, and deep learning approaches. These "black-box" approaches rarely provide insights into the underlying physiological mechanisms and are not easily explainable, which may prevent their use in clinical practice. Alternatively, simple methods using meaningful features, preferably using a single feature (biomarker), are highly desirable for assessing the danger of premature birth. To identify suitable biomarker candidates, we performed feature selection using the stabilized sequential-forward feature-selection method employing learning and validation sets, and using multiple standard classifiers and multiple sets of the most widely used features derived from EHG signals. The most promising single feature to classify between premature EHG records and EHG records of all other term delivery modes evaluated on the test sets appears to be Peak Amplitude of the normalized power spectrum (PA) of the EHG signal in the low frequency band (0.125-0.575 Hz) which closely matches the known Fast Wave Low (FWL) frequency band. For classification of EHG records of the publicly available TPEHG DB, TPEHGT DS, and ICEHG DS databases, using the Partition-Synthesis evaluation technique, the proposed single feature, PA, achieved Classification Accuracy (CA) of 76.5% (AUC of 0.81). In combination with the second most promising feature, Median Frequency (MF) of the power spectrum in the frequency band above 1.0 Hz, which relates to the maternal resting heart rate, CA increased to 78.0% (AUC of 0.86). The developed method in this study for the prediction of premature birth outperforms single-feature and many multi-feature methods based on the EHG, and existing non-invasive chemical and molecular biomarkers. The developed method is fully automatic, simple, and the two proposed features are explainable.
目前，基于子宫肌电图（EHG）即电子宫描记术（EHG）的非侵入性早产预测方法的发展趋势表明，越来越多地使用大量特征、复杂模型和深度学习方法。这些“黑盒”方法很少提供对潜在生理机制的深入了解，也不容易解释，这可能会阻止它们在临床实践中的使用。相反，使用有意义特征的简单方法，最好使用单个特征（生物标志物），非常适合评估早产的危险。为了识别合适的生物标志物候选者，我们使用稳定的顺序前向特征选择方法，使用学习和验证集，并使用多个标准分类器和从 EHG 信号中导出的多个最广泛使用的特征集，对早产 EHG 记录和所有其他足月分娩模式的 EHG 记录进行分类。在测试集中评估的最有前途的单个特征似乎是 EHG 信号在低频带（0.125-0.575 Hz）的归一化功率谱的峰幅度（PA），它与已知的快速波低频（FWL）频带非常匹配。对于使用 Partition-Synthesis 评估技术的公开可用 TPEHG DB、TPEHGT DS 和 ICEHG DS 数据库中的 EHG 记录分类，所提出的单个特征 PA 实现了 76.5%的分类准确性（AUC 为 0.81）。与第二个最有前途的特征，即高于 1.0 Hz 的频带中的功率谱的中值频率（MF）结合使用，该特征与母体静息心率有关，CA 增加到 78.0%（AUC 为 0.86）。本研究中用于预测早产的方法优于基于 EHG 的单一特征和许多多特征方法，以及现有的非侵入性化学和分子生物标志物。所开发的方法完全自动、简单，并且所提出的两个特征是可解释的。
