ArticleBiocybernetics and biomedical engineering
Application of decision tree in determining the importance of surface electrohysterography signal characteristics for recognizing uterine contractions.
Article in Biocybernetics and biomedical engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Analysis of electrohysterogram signals for predicting obstetric outcome using machine learning methods: a scoping review.BMC pregnancy and childbirth · 2026Article
- Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks.Sensors (Basel, Switzerland) · 2023Article
- Prediction of Preterm Delivery from Unbalanced EHG Database.Sensors (Basel, Switzerland) · 2022Article
- IDENTIFICATION OF UTERINE CONTRACTIONS BY AN ENSEMBLE OF GAUSSIAN PROCESSES.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2021Article
- Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate.Scientific reports · 2021Article
- Acquisition and Analysis of Electrohysterogram Signal.Journal of medical systems · 2020Article
- Cervical Dilation Classification from Electrohysterography and Clinical Features: A Machine-Learning-Derived Digital Biomarker.Digital biomarkersArticle
- Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest.Biocybernetics and biomedical engineeringArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aims of this study were to apply decision tree to classify uterine activities (contractions and non-contractions) using the waveform characteristics derived from different channels of electrohysterogram (EHG) signals and then rank the importance of these characteristics. Both the tocodynamometer (TOCO) and 8-channel EHG signals were simultaneously recorded from 34 healthy pregnant women within 24 h before delivery. After preprocessing of EHG signals, EHG segments corresponding to the uterine contractions and non-contractions were manually extracted from both original and normalized EHG signals according to the TOCO signals and the human marks. 24 waveform characteristics of the EHG segments were derived separately from each channel to train the decision tree and classify the uterine activities. The results showed the Power and sample entropy (SamEn) extracted from the un-normalized EHG segments played the most important roles in recognizing uterine activities. In addition, the EHG signal characteristics from channel 1 produced better classification results (AUC = 0.75, Sensitivity = 0.84, Specificity = 0.78, Accuracy = 0.81) than the others. In conclusion, decision tree could be used to classify the uterine activities, and the Power and SamEn of un-normalized EHG segments were the most important characteristics in uterine contraction classification.
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