Held Claudio M, Causa Javier, Causa Leonardo, Estévez Pablo A, Perez Claudio A, Garrido Marcelo, Chamorro Rodrigo, Algarin Cecilia, Peirano Patricio
Department of Electrical Engineering, Universidad de Chile, Santiago, Chile.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2267-70. doi: 10.1109/EMBC.2012.6346414.
We present an automated multiple-step tool to identify Rapid Eye Movements (REMs) in the polysomnogram, based on modeling expert criteria. It begins by identifying the polysomnogram segments compatible with REMs presence. On these segments, high-energy REMs are identified. Then, vicinity zones around those REMs are defined, and lesser-energy REMs are sought in these vicinities. This strategy has the advantage that it can detect lesser-energy REMs without increasing much the false positive detections. Signal processing, feature extraction, and fuzzy logic tools are used to achieve the goal. The tool was trained and validated on a database consisting of 20 all-night polysomnogram recordings (160 hr) of healthy ten-year-old children. Preliminary results on the validation set show 85.5% sensitivity and a false positive rate of 16.2%. Our tool works on complete polysomnogram recordings, without the need of preprocessing, prior knowledge of the hypnogram, or noise-free segments selection.
我们基于对专家标准的建模，提出了一种用于识别多导睡眠图中快速眼动（REM）的自动化多步骤工具。它首先识别与快速眼动存在兼容的多导睡眠图片段。在这些片段上，识别出高能量的快速眼动。然后，定义这些快速眼动周围的邻近区域，并在这些邻近区域中寻找能量较低的快速眼动。这种策略的优点是，它可以检测到能量较低的快速眼动，而不会大幅增加误报检测。使用信号处理、特征提取和模糊逻辑工具来实现这一目标。该工具在一个由20份健康十岁儿童的全夜多导睡眠图记录（160小时）组成的数据库上进行了训练和验证。验证集的初步结果显示灵敏度为85.5%，误报率为16.2%。我们的工具可处理完整的多导睡眠图记录，无需预处理、睡眠分期的先验知识或无噪声片段选择。
