Data for: Integrating Machine Learning Techniques and Physiology Based Heart Rate Features for Antepartum Fetal Monitoring

The article contains a set of 12 linear and nonlinear indices extracted from Fetal Heart Rate (FHR) recordings by means of CTG monitors on two groups of fetuses: 60 normals and 60 Intra Uterine Growth Restricted (IUGR) fetuses. The two populations were selected by clinicians after birth on the basis of clinical standards for detecting growth restricted newborns (Apgar scores, percentile weight, …). The indices were computed on FHR recordings, each one lasting more than 30 minutes, by means of algorithms already published in the scientific literature.本文包含一组共12项线性与非线性指标，这些指标通过胎心宫缩图（CTG）监护仪采集的胎儿心率（Fetal Heart Rate, FHR）记录提取得到，对应两组胎儿：60名正常胎儿与60名宫内生长受限（Intra Uterine Growth Restricted, IUGR）胎儿。该两组研究对象均由临床医师在新生儿出生后，依据检测生长受限新生儿的临床标准（如阿普加评分、体重百分位数等）筛选确定。所有指标均基于时长超过30分钟的FHR记录，通过已发表于科学文献中的算法计算得到。