School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Qld. 4067, Australia.
IEEE Trans Biomed Eng. 2010 May;57(5):1108-16. doi: 10.1109/TBME.2009.2038362. Epub 2010 Feb 17.
Breathing patterns are characteristically different between infant active sleep (AS) and quiet sleep (QS), and statistical quantifications of interbreath interval (IBI) data have previously been used to discriminate between infant sleep states. It has also been identified that breathing patterns are governed by a nonlinear controller. This study aims to investigate whether nonlinear quantifications of infant IBI data are characteristically different between AS and QS, and whether they may be used to discriminate between these infant sleep states. Polysomnograms were obtained from 24 healthy infants at six months of age. Periods of AS and QS were identified, and IBI data extracted. Recurrence quantification analysis (RQA) was applied to each period, and recurrence calculated for a fixed radius in the range of 0-8 in steps of 0.02, and embedding dimensions of 4, 6, 8, and 16. When a threshold classifier was trained, the RQA variable recurrence was able to correctly classify 94.3% of periods in a test dataset. It was concluded that RQA of IBI data is able to accurately discriminate between infant sleep states. This is a promising step toward development of a minimal-channel automatic sleep state classification system.
呼吸模式在婴儿活跃睡眠 (AS) 和安静睡眠 (QS) 之间存在明显差异，并且先前已经使用呼吸间隔 (IBI) 数据的统计量化来区分婴儿的睡眠状态。还已经确定呼吸模式由非线性控制器控制。本研究旨在调查婴儿 IBI 数据的非线性量化在 AS 和 QS 之间是否存在明显差异，以及它们是否可用于区分这些婴儿的睡眠状态。从 24 名 6 个月大的健康婴儿中获得了多导睡眠图。确定了 AS 和 QS 期，并提取了 IBI 数据。对每个时期应用了递归定量分析 (RQA)，并在 0-8 的范围内以 0.02 的步长计算了固定半径的递归，嵌入维度为 4、6、8 和 16。当训练阈值分类器时，RQA 变量递归能够正确分类测试数据集中的 94.3%的时期。结论是，IBI 数据的 RQA 能够准确区分婴儿的睡眠状态。这是朝着开发最小通道自动睡眠状态分类系统迈出的有希望的一步。
