Delivery Type (Induced or Cesarean) Classification with Deep Learning Method Using Shannon Entropy of Electrohysterogram Signal
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".