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

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.

Spectrotemporal Feature Extraction in EHG Signals and Tocograms for Enhanced Preterm Birth Prediction

Preterm birth (PTB), defined as delivery before 37 weeks of gestation, is a leading cause of neonatal mortality and long term health complications. Early detection is essential for enabling timely medical interventions. Electrohysterography (EHG) and tocography (TOCO) are promising non invasive tools for PTB prediction, but prior studies often suffer from class imbalance, improper oversampling, and reliance on features with limited physiological relevance. This work presents a machine learning pipeline incorporating robust preprocessing, physiologically grounded feature extraction, and rigorous evaluation. Features were extracted from EHG (and TOCO) signals using Mel frequency cepstral coefficients, statistical descriptors of wavelet coefficients, and peaks of the normalized power spectrum. Signal quality was enhanced via Karhunen Lo\`eve Transform (KLT) denoising through eigenvalue based subspace decomposition. Multiple classifiers, including Logistic Regression, Support Vector Machines, Random Forest, Gradient Boosting, Multilayer Perceptron, and CatBoost, were evaluated on the TPEHGT dataset. The CatBoost classifier with KLT denoising achieved the highest performance on fixed interval segments of the TPEHGT dataset, reaching 97.28% accuracy and an AUC of 0.9988. Ablation studies confirmed the critical role of both KLT denoising and physiologically informed features. Comparative analysis showed that including TOCO signals did not substantially improve prediction over EHG alone, highlighting the sufficiency of EHG for PTB detection. These results demonstrate that combining denoising with domain relevant features can yield highly accurate, robust, and clinically interpretable models, supporting the development of cost effective and accessible PTB prediction tools, particularly in low resource healthcare settings.