Cohen Gregory, de Chazal Philip
MARCS Institute, University of Western Sydney, Australia.
MARCS Institute, University of Western Sydney, Australia; School of Electrical and Information Engineering, University of Sydney, Australia.
Comput Biol Med. 2015 Aug;63:118-23. doi: 10.1016/j.compbiomed.2015.05.007. Epub 2015 May 22.
This study explores the use and applicability of two minimally invasive sensors, electrocardiogram (ECG) and pulse oximetry, in addressing the high costs and difficulty associated with the early detection of sleep apnea hypopnea syndrome in infants. An existing dataset of 396 scored overnight polysomnography recordings were used to train and test a linear discriminants classifier. The dataset contained data from healthy infants, infants diagnosed with sleep apnea, infants with siblings who had died from sudden infant death syndrome (SIDS) and pre-term infants. Features were extracted from the ECG and pulse-oximetry data and used to train the classifier. The performance of the classifier was evaluated using a leave-one-out cross-validation scheme and an accuracy of 66.7% was achieved, with a specificity of 67.0% and a sensitivity of 58.1%. Although the performance of the system is not yet at the level required for clinical use, this work forms an important step in demonstrating the validity and potential for such low-cost and minimally invasive diagnostic systems.
本研究探讨了两种微创传感器，即心电图（ECG）和脉搏血氧饱和度测定法，在解决婴儿睡眠呼吸暂停低通气综合征早期检测相关的高成本和困难方面的应用及适用性。使用一个包含396份经评分的夜间多导睡眠图记录的现有数据集来训练和测试线性判别分类器。该数据集包含来自健康婴儿、被诊断为睡眠呼吸暂停的婴儿、有死于婴儿猝死综合征（SIDS）的兄弟姐妹的婴儿以及早产儿的数据。从心电图和脉搏血氧饱和度数据中提取特征，并用于训练分类器。使用留一法交叉验证方案评估分类器的性能，准确率达到66.7%，特异性为67.0%，灵敏度为58.1%。尽管该系统的性能尚未达到临床使用所需的水平，但这项工作是证明此类低成本和微创诊断系统的有效性和潜力的重要一步。
