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经验模态分解与可解释机器学习用于基于子宫电描记图的早产分类

Empirical mode decomposition and interpretable machine learning for preterm birth classification from electrohysterography

Umesha Tilakarathna, Senith Jayakody, Kalana Jayasooriya, Roshan Godaliyadda, Parakrama Ekanayake, Isuru Nawinne, Chathura Rathnayake

arXiv 2608.17643首次发表:更新:

AI 中文总结

本研究基于TPEHGT数据集,采用EMD提取EHG的IMF1特征,结合随机森林分类实现早产分类,性能优于时域特征,为早产无创评估提供了新方法。

AI 中文摘要

早产(PTB)仍是全球重大健康问题,可靠的无创风险评估仍存在难度。子宫电描记图(EHG)可从孕妇腹部记录子宫电活动,或可支持早产评估,但当同一记录的片段被拆分至训练与验证折时,性能可能被高估。本研究使用公开TPEHGT数据集的26份妊娠记录(13份早产、13份足月产),评估经验模态分解(EMD)用于足月产与早产分类的效果。研究对比了标注区间与非重叠的固定3分钟窗口,评估了前四个本征模态函数(IMFs)。对每个EHG通道的14个特征,采用9种分类器,结合重复5折记录分组交叉验证与记录级聚合进行评估。IMF1的平均性能最强,采用固定3分钟IMF1特征时,随机森林的平均准确率为0.8308、F1值为0.7969、平衡准确率为0.8308、MCC为0.6998、ROC-AUC为0.8157、平均精度为0.8877。在所有报告的平均指标中,IMF1均优于匹配滤波的时域特征。早产记录显示出更小、间隔更规律的类峰事件,更低的时间能量指标和更高的熵。这些发现支持在更大的独立队列中进一步评估基于IMF1的EHG分类方法。

英文摘要

Preterm birth (PTB) remains a major global health problem, and reliable non-invasive risk assessment remains difficult. Electrohysterography (EHG) records uterine electrical activity from the maternal abdomen and may support PTB assessment, but performance can be inflated when segments from the same recording are split across training and validation folds. We evaluated empirical mode decomposition (EMD) for term-versus-preterm classification using 26 pregnancy recordings (13 preterm, 13 term) from the public TPEHGT dataset. Annotated intervals and non-overlapping fixed 3-minute windows were compared, and the first four intrinsic mode functions (IMFs) were evaluated. Fourteen features from each of three EHG channels were assessed with nine classifiers using repeated five-fold recording-grouped cross-validation and recording-level aggregation. IMF1 gave the strongest mean performance. With fixed 3-minute IMF1 features, Random Forest achieved mean accuracy 0.8308, F1 0.7969, balanced accuracy 0.8308, MCC 0.6998, ROC-AUC 0.8157, and average precision 0.8877. IMF1 also outperformed matched filtered time-domain features across all reported mean metrics. Preterm recordings showed smaller, more regularly spaced peak-like events, lower temporal-energy measures, and higher entropy. These findings support further evaluation of IMF1-based EHG classification in larger independent cohorts.

Comments33 pages, 9 figures. Submitted to PLOS ONE. Umesha Tilakarathna and Senith Jayakody contributed equally to this work

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