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.

本研究的目的是应用决策树，利用来自不同通道的子宫电图 (EHG) 信号的波形特征对子宫活动（宫缩和非宫缩）进行分类，然后对这些特征的重要性进行排序。同时记录了 34 名健康孕妇在分娩前 24 小时内的生育力计 (TOCO) 和 8 通道 EHG 信号。对EHG信号进行预处理后，根据TOCO信号和人类标记，从原始EHG信号和标准化EHG信号中手动提取对应于子宫收缩和非收缩的EHG片段。从每个通道分别导出 EHG 片段的 24 个波形特征，以训练决策树并对子宫活动进行分类。结果表明，从非标准化 EHG 片段中提取的功率和样本熵 (SamEn) 在识别子宫活动中发挥着最重要的作用。此外，通道 1 的 EHG 信号特征比其他通道产生了更好的分类结果（AUC = 0.75，灵敏度 = 0.84，特异性 = 0.78，准确度 = 0.81）。总之，决策树可用于对子宫活动进行分类，非标准化EHG片段的Power和SamEn是子宫收缩分类中最重要的特征。