IEEE J Biomed Health Inform. 2021 Apr;25(4):1006-1017. doi: 10.1109/JBHI.2020.3021662. Epub 2021 Apr 6.
This study was designed to test the diagnostic value of visibility graph features derived from the heart rate time series to predict late onset sepsis (LOS) in preterm infants using machine learning.
The heart rate variability (HRV) data was acquired from 49 premature newborns hospitalized in neonatal intensive care units (NICU). The LOS group consisted of patients who received more than five days of antibiotics, at least 72 hours after birth. The control group consisted of infants who did not receive antibiotics. HRV features in the days prior to the start of antibiotics (LOS group) or in a randomly selected period (control group) were compared against a baseline value calculated during a calibration period. After automatic feature selection, four machine learning algorithms were trained. All the tests were done using two variants of the feature set: one only included traditional HRV features, and the other additionally included visibility graph features. Performance was studied using area under the receiver operating characteristics curve (AUROC).
The best performance for detecting LOS was obtained with logistic regression, using the feature set including visibility graph features, with AUROC of 87.7% during the six hours preceding the start of antibiotics, and with predictive potential (AUROC above 70%) as early as 42 h before start of antibiotics.
These results demonstrate the usefulness of introducing visibility graph indexes in HRV analysis for sepsis prediction in newborns.
The method proposed the possibility of non-invasive, real-time monitoring of risk of LOS in a NICU setting.
本研究旨在通过机器学习测试从心率时间序列中提取的可视性图特征对早产儿迟发性败血症（LOS）的诊断价值。
从住院于新生儿重症监护病房（NICU）的 49 名早产儿中获取心率变异性（HRV）数据。LOS 组包括接受抗生素治疗超过 5 天的患者，至少在出生后 72 小时。对照组由未接受抗生素治疗的婴儿组成。在开始使用抗生素（LOS 组）之前或在随机选择的时期（对照组）的前几天比较 HRV 特征与在校准期间计算的基线值。在自动特征选择之后，训练了四种机器学习算法。所有测试均使用两种特征集变体进行：一种仅包含传统 HRV 特征，另一种另外包含可视性图特征。使用接收器操作特征曲线（AUROC）下的面积来研究性能。
使用包括可视性图特征的特征集，逻辑回归在检测 LOS 方面表现最佳，在开始使用抗生素前的 6 小时内 AUROC 为 87.7%，并且在开始使用抗生素前 42 小时就具有预测潜力（AUROC 高于 70%）。
这些结果表明，在新生儿败血症预测中引入 HRV 分析中的可视性图指数是有用的。
该方法提出了在 NICU 环境中进行 LOS 风险非侵入性、实时监测的可能性。
