Interdisciplinary Unit of Biotechnology (UPIBI), National Polytechnic Institute (IPN) of Mexico, Mexico City, Mexico.
National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico.
Front Endocrinol (Lausanne). 2023 Jan 10;13:1035615. doi: 10.3389/fendo.2022.1035615. eCollection 2022.
Despite vast research, premature birth's electrophysiological mechanisms are not fully understood. Prediction of preterm birth contributes to child survival by providing timely and skilled care to both mother and child. Electrohysterography is an affordable, noninvasive technique that has been highly sensitive in diagnosing preterm labor. This study aimed to choose the more appropriate combination of characteristics, such as electrode channel and bandwidth, as well as those linear, time-frequency, and nonlinear features of the electrohysterogram (EHG) for predicting preterm birth using classifiers.
We analyzed two open-access datasets of 30 minutes of EHG obtained in regular checkups of women around 31 weeks of pregnancy who experienced premature labor (P) and term labor (T). The current approach filtered the raw EHGs in three relevant frequency subbands (0.3-1 Hz, 1-2 Hz, and 2-3Hz). The EHG time series were then segmented to create 120-second windows, from which individual characteristics were calculated. The linear, time-frequency, and nonlinear indices of EHG of each combination (channel-filter) were fed to different classifiers using feature selection techniques.
The best performance, i.e., 88.52% accuracy, 83.83% sensitivity, and 93.22% specificity, was obtained in the 2-3 Hz bands using Medium Frequency, Continuous Wavelet Transform (CWT), and entropy-based indices. Interestingly, CWT features were significantly different in all filter-channel combinations. The proposed study uses small samples of EHG signals to diagnose preterm birth accurately, showing their potential application in the clinical environment.
Our results suggest that CWT and novel entropy-based features of EHG could be suitable descriptors for analyzing and understanding the complex nature of preterm labor mechanisms.
尽管进行了广泛的研究，但早产的电生理机制仍未完全被理解。早产的预测通过为母婴提供及时和熟练的护理来有助于儿童的生存。电子宫描记术是一种经济实惠、非侵入性的技术，在诊断早产方面具有高度的敏感性。本研究旨在通过分类器选择更合适的电子宫描记图（EHG）特征组合，例如电极通道和带宽，以及 EHG 的线性、时频和非线性特征，以预测早产。
我们分析了两个公开获取的数据集，这些数据集包含了 31 周左右经历早产（P）和足月产（T）的孕妇的 30 分钟 EHG。当前的方法在三个相关的频率子带（0.3-1 Hz、1-2 Hz 和 2-3 Hz）中对原始 EHG 进行滤波。EHG 时间序列然后被分段以创建 120 秒的窗口，从中计算出单个特征。使用特征选择技术将 EHG 的线性、时频和非线性指数输入到不同的分类器中。
在使用中频、连续小波变换（CWT）和基于熵的指数的 2-3 Hz 频段中，获得了最佳的性能，即 88.52%的准确率、83.83%的灵敏度和 93.22%的特异性。有趣的是，CWT 特征在所有滤波器-通道组合中都有显著差异。本研究使用小样本的 EHG 信号来准确诊断早产，表明它们在临床环境中的潜在应用。
我们的结果表明，EHG 的 CWT 和新颖的基于熵的特征可能是分析和理解早产机制复杂性的合适描述符。
