Background and objective

Preterm neonates are prone to episodes of apnea, bradycardia and hypoxia (ABH) that can lead to neurological morbidities or even death. There is broad interest in developing methods for real-time prediction of ABH events to inform interventions that prevent or reduce their incidence and severity. Using advances in machine learning methods, this study develops an algorithm to predict ABH events.

Methods

Following previous studies showing that respiratory instabilities are closely associated with bouts of movement, we present a modeling framework that can predict ABH events using both movement and cardio-respiratory features derived from routine clinical recordings. In 10 preterm infants, movement onsets and durations were estimated with a wavelet-based algorithm that quantified artifactual distortions of the photoplethysmogram signal. For prediction, cardio-respiratory features were created from time-delayed correlations of inter-beat and inter-breath intervals with past values; movement features were derived from time-delayed correlations with inter-breath intervals. Gaussian Mixture Models and Logistic Regression were used to develop predictive models of apneic events. Performance of the models was evaluated with ROC curves.

Results

Performance of the prediction framework (mean AUC) was 0.77 ± 0.04 for 66 ABH events on training data from 7 infants. When grouped by the severity of the associated bradycardia during the ABH event, the framework was able to predict 83% and 75% of the most severe episodes in the 7-infant training set and 3-infant test set, respectively. Notably, inclusion of movement features significantly improved the predictions compared with modeling with only cardio-respiratory signals.

Conclusions

Our findings suggest that recordings of movement provide important information for predicting ABH events in preterm infants, and can inform preemptive interventions designed to reduce the incidence and severity of ABH events.

中文翻译：

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使用心肺和运动特征预测早产儿的呼吸暂停事件

背景和目标

早产新生儿容易出现呼吸暂停、心动过缓和缺氧 (ABH)，这可能导致神经系统疾病甚至死亡。人们广泛关注开发实时预测 ABH 事件的方法，以便为预防或减少其发生率和严重程度的干预措施提供信息。利用机器学习方法的进步，本研究开发了一种算法来预测 ABH 事件。

方法

根据先前的研究表明呼吸不稳定与运动发作密切相关，我们提出了一个建模框架，该框架可以使用来自常规临床记录的运动和心肺功能来预测 ABH 事件。在 10 名早产儿中，使用基于小波的算法估计运动开始和持续时间，该算法量化光电体积描记图信号的人为失真。为了进行预测，心肺功能是根据节拍间和呼吸间间隔与过去值的时间延迟相关性创建的；运动特征来源于与呼吸间期的时间延迟相关性。高斯混合模型和逻辑回归被用来开发呼吸暂停事件的预测模型。使用 ROC 曲线评估模型的性能。

结果

对于来自 7 个婴儿的训练数据的 66 个 ABH 事件，预测框架的性能（平均 AUC）为 0.77 ± 0.04。当按 ABH 事件期间相关心动过缓的严重程度分组时，该框架能够分别预测 7 名婴儿训练集和 3 名婴儿测试集中 83% 和 75% 的最严重发作。值得注意的是，与仅使用心肺信号建模相比，包含运动特征显着改善了预测。

结论

我们的研究结果表明，运动记录为预测早产儿 ABH 事件提供了重要信息，并且可以为旨在降低 ABH 事件的发生率和严重程度的先发制人干预提供信息。