Cardiovascular oscillations at the bedside: early diagnosis of neonatal sepsis using heart rate characteristics monitoring.
Cardiovascular oscillations at the bedside: early diagnosis of neonatal sepsis using heart rate characteristics monitoring.
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DOI：
10.1088/0967-3334/32/11/s08
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发表时间：
2011-11
影响因子：
3.2
通讯作者：
Lake DE
中科院分区：
文献类型：
--
作者：
Moorman JR;Delos JB;Flower AA;Cao H;Kovatchev BP;Richman JS;Lake DE
我们已经应用统计信号处理和非线性动力学的原理来分析早产儿的心率时间序列，以协助败血症的早期诊断，这是一种常见的和潜在致命的血液细菌感染。我们首先观察了在临床症状出现前数小时至数天内心率间隔时间序列的变异性降低和短暂减速。我们发现，标准偏差、样本不对称和样本熵的测量与即将发生的临床疾病高度相关。我们开发了多变量统计预测模型，以及一个向临床医生显示实时结果的界面。使用这种方法，我们已经观察到许多病例，其中早期新生儿败血症被诊断和治疗没有任何临床疾病。这篇综述的重点是数学和统计时间序列方法用于检测这些异常心率特征和提供预测监测信息给临床医生。
We have applied principles of statistical signal processing and non-linear dynamics to analyze heart rate time series from premature newborn infants in order to assist in the early diagnosis of sepsis, a common and potentially deadly bacterial infection of the bloodstream. We began with the observation of reduced variability and transient decelerations in heart rate interval time series for hours up to days prior to clinical signs of illness. We find that measurements of standard deviation, sample asymmetry and sample entropy are highly related to imminent clinical illness. We developed multivariable statistical predictive models, and an interface to display the real-time results to clinicians. Using this approach, we have observed numerous cases in which incipient neonatal sepsis was diagnosed and treated without any clinical illness at all. This review focuses on the mathematical and statistical time series approaches used to detect these abnormal heart rate characteristics and present predictive monitoring information to the clinician.