Akademik Veri Yönetim Sistemi
TRAITEMENT DU SIGNAL, cilt.42, sa.4, ss.2107-2118, 2025 (SCI-Expanded)
Current methods in the literature for predicting preterm birth using electrohysterogram (EHG) signals generally concentrate only on classifying term and preterm spontaneous deliveries. However, a realistic approach should also include other delivery types, such as induced, cesarean, and induced-cesarean sections. We can increase the precision of labor stage identification and better understand preterm birth risk by examining the characteristics of EHG signals unique to various delivery methods. In this study, the methodology involves preprocessing EHG signals and extracting key features through Shannon Entropy and logarithmic energy. These features, which do not need complicated models, are effective in highlighting the key traits and complexity of the signals. Thanks to their high sensitivity, they can identify even the most subtle shifts in uterine activity. The adaptive synthetic (ADASYN) oversampling technique is applied after extracting features to address the class imbalance. These features are then examined using three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) to evaluate their effectiveness in distinguishing different types of delivery. The ICEHG-DS database was used to evaluate the performance of the proposed method, and the best results were achieved for the LSTM method using the Shannon Entropy feature extracted from channel S3, yielding an average F1-score of 99.34% and an accuracy of 99.33%. This work demonstrates the feasibility of accurately predicting the type of delivery by analyzing EHG signals as early as the 23rd week of pregnancy, utilizing a feature extraction method with low computational complexity.