CIBER de Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain.
Physiol Meas. 2012 Sep;33(9):1503-16. doi: 10.1088/0967-3334/33/9/1503. Epub 2012 Aug 17.
Respiratory signals monitored in the neonatal intensive care units are usually ignored due to the high prevalence of noise and false alarms (FA). Apneic events are generally therefore indicated by a pulse oximeter alarm reacting to the subsequent desaturation. However, the high FA rate in the photoplethysmogram may desensitize staff, reducing the reaction speed. The main reason for the high FA rates of critical care monitors is the unimodal analysis behaviour. In this work, we propose a multimodal analysis framework to reduce the FA rate in neonatal apnoea monitoring. Information about oxygen saturation, heart rate, respiratory rate and signal quality was extracted from electrocardiogram, impedance pneumogram and photoplethysmographic signals for a total of 20 features in the 5 min interval before a desaturation event. 1616 desaturation events from 27 neonatal admissions were annotated by two independent reviewers as true (physiologically relevant) or false (noise-related). Patients were divided into two independent groups for training and validation, and a support vector machine was trained to classify the events as true or false. The best classification performance was achieved on a combination of 13 features with sensitivity, specificity and accuracy of 100% in the training set, and a sensitivity of 86%, a specificity of 91% and an accuracy of 90% in the validation set.
新生儿重症监护病房中监测到的呼吸信号通常由于噪声和误报（FA）的高发生率而被忽略。因此，呼吸暂停事件通常由脉搏血氧仪报警指示，该报警对随后的饱和度降低做出反应。然而，光容积描记图中的高 FA 率可能会使工作人员失去敏感性，降低反应速度。重症监护监测器高 FA 率的主要原因是单峰分析行为。在这项工作中，我们提出了一种多峰分析框架，以降低新生儿呼吸暂停监测中的 FA 率。从心电图、阻抗呼吸图和光容积描记图信号中提取了氧饱和度、心率、呼吸率和信号质量信息，在饱和度降低事件发生前的 5 分钟间隔内共提取了 20 个特征。由两名独立的评审员对 27 名新生儿入院的 1616 个饱和度降低事件进行了注释，这些事件被标记为真（与生理相关）或假（与噪声相关）。患者被分为两个独立的组进行训练和验证，支持向量机被训练用于对事件进行真假分类。在训练集中，使用 13 个特征的最佳分类性能实现了 100%的敏感性、特异性和准确性，而在验证集中，敏感性为 86%，特异性为 91%，准确性为 90%。
