MIT Lincoln Laboratory, Lexington, MA, United States.
Respir Physiol Neurobiol. 2013 Nov 1;189(2):223-31. doi: 10.1016/j.resp.2013.05.034. Epub 2013 Jun 2.
Apnea of prematurity is a common disorder of respiratory control among preterm infants, with potentially serious adverse consequences on infant development. We review the capability for automatically assessing apnea risk and predicting apnea episodes from multimodal physiological measurements, and for using this knowledge to provide timely therapeutic intervention. We also review other, similar clinical domains of respiratory distress assessment and prediction in the hope of gaining useful insights. We propose an algorithmic framework for constructing discriminative feature vectors from physiological measurements, and for building robust and effective statistical models for apnea assessment and prediction.
早产儿呼吸暂停是早产儿呼吸控制的常见障碍，可能对婴儿发育产生严重的不良后果。我们回顾了从多模态生理测量中自动评估呼吸暂停风险和预测呼吸暂停发作的能力，以及利用这些知识提供及时治疗干预的能力。我们还回顾了其他类似的呼吸窘迫评估和预测的临床领域，希望从中获得有用的见解。我们提出了一种从生理测量中构建判别特征向量的算法框架，并构建了用于呼吸暂停评估和预测的稳健有效的统计模型。
