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.
Bibliographical noteThis is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).
- Electrohysterogram (EHG)
- Decision tree
- Uterine contraction
Hao, D., Qiu, Q., Zhou, X., An, Y., Peng, J., Yang, L., & Zheng, D. (2019). Application of decision tree in determining the importance of surface electrohysterography signal characteristics for recognizing uterine contractions. Biocybernetics and Biomedical Engineering, 806-813. https://doi.org/10.1016/j.bbe.2019.06.008