Holzmann C A, Pérez C A, Held C M, San Martín M, Pizarro F, Pérez J P, Garrido M, Peirano P
Departamento. Ingeniería Eléctrica, Universidad de Chile, Santiago, Chile.
Med Biol Eng Comput. 1999 Jul;37(4):466-76. doi: 10.1007/BF02513332.
This work is part of a project to develop an expert system for automated classification of the sleep/waking states in human infants; i.e. active or rapid-eye-movement sleep (REM), quiet or non-REM sleep (NREM), including its four stages, indeterminate sleep (IS) and wakefulness (WA). A model to identify these states, introducing an objective formalisation in terms of the state variables characterising the recorded patterns, is presented. The following digitally recorded physiological events are taken into account to classify the sleep/waking states: predominant background activity and the existence of sleep spindles in the electro-encephalogram; existence of rapid eye movements in the electro-oculogram; and chin muscle tone in the electromyogram. Methods to detect several of these parameters are described. An expert system based on artificial ganglionar lattices is used to classify the sleep/waking states, on an off-line minute-by-minute basis. Algorithms to detect patterns automatically and an expert system to recognise sleep/waking states are introduced, and several adjustments and tests using various real patients are carried out. Results show an overall performance of 96.4% agreement with the expert on validation data without artefacts, and 84.9% agreement on validation data with artefacts. Moreover, results show a significant improvement in the classification agreement due to the application of the expert system, and a discussion is carried out to justify the difficulties of matching the expert's criteria for the interpretation of characterising patterns.
这项工作是一个项目的一部分，该项目旨在开发一个用于自动分类人类婴儿睡眠/清醒状态的专家系统；即活跃或快速眼动睡眠（REM）、安静或非快速眼动睡眠（NREM），包括其四个阶段、不确定睡眠（IS）和清醒（WA）。本文提出了一个识别这些状态的模型，该模型根据表征记录模式的状态变量引入了客观的形式化方法。为了对睡眠/清醒状态进行分类，考虑了以下数字记录的生理事件：脑电图中的主要背景活动和睡眠纺锤波的存在；眼电图中快速眼动的存在；以及肌电图中的下巴肌张力。描述了检测其中几个参数的方法。一个基于人工神经节晶格的专家系统用于逐分钟离线分类睡眠/清醒状态。介绍了自动检测模式的算法和识别睡眠/清醒状态的专家系统，并对多个真实患者进行了多次调整和测试。结果表明，在无伪迹的验证数据上，总体性能与专家的一致性为96.4%，在有伪迹的验证数据上为84.9%。此外，结果表明，由于应用了专家系统，分类一致性有了显著提高，并进行了讨论以说明匹配专家对特征模式解释标准的困难。
