Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks for recognizing the preterm delivery using random forest (RF). EHG signals from 300 pregnant women were divided into two groups depending on when the signals were recorded: i) preterm and term delivery with EHG recorded before the 26^th week of gestation (denoted by PE and TE group), and ii) preterm and term delivery with EHG recorded during or after the 26^th week of gestation (denoted by PL and TL group). 31 linear features and nonlinear features were derived from each EHG signal, and then compared comprehensively within PE and TE group, and PL and TL group. After employing the adaptive synthetic sampling approach and six-fold cross-validation, the accuracy (ACC), sensitivity, specificity and area under the curve (AUC) were applied to evaluate RF classification. For PL and TL group, RF achieved the ACC of 0.93, sensitivity of 0.89, specificity of 0.97, and AUC of 0.80. Similarly, their corresponding values were 0.92, 0.88, 0.96 and 0.88 for PE and TE group, indicating that RF could be used to recognize preterm delivery effectively with EHG signals recorded before the 26^th week of gestation.

开发用于识别早产的计算方法对于及时诊断和治疗早产很重要。这项研究的主要目的是评估在不同孕周记录的子宫电图（EHG）信号，以便使用随机森林（RF）识别早产。从300名孕妇EHG信号被分成两组，这取决于当信号被记录在：ⅰ）早产和足月分娩与EHG 26之前记录^日妊娠（由PE和TE组表示）的星期，以及ii）早产儿和术语在26^日或之后记录的EHG交付情况妊娠一周（由PL和TL组表示）。从每个EHG信号中得出31个线性特征和非线性特征，然后在PE和TE组，PL和TL组中进行全面比较。在采用自适应合成采样方法和六重交叉验证后，将准确性（ACC），灵敏度，特异性和曲线下面积（AUC）用于评估RF分类。对于PL和TL组，RF的ACC为0.93，敏感性为0.89，特异性为0.97，AUC为0.80。类似地，其对应的值分别为0.92，0.88，0.96和0.88 PE和TE组，表明RF可以用来识别与26之前记录EHG信号有效早产^个妊娠周。