EXPERT SYSTEM FOR AUTOMATED SLEEP STAGE SCORING

描述：(改编自申请人的摘要)：实施和 提出了一个计算机化的专家系统睡眠评分的验证。 这样的系统将提高睡眠实验室的生产率，并且 在降低睡眠诊断成本的同时提高质量 程序。第一阶段的研究将集中在基本算法上 自动睡眠阶段评分。这些算法是基于贝叶斯的 决策理论，并遵循人类专家的决策步骤 根据Rechtschaffen-Kales标准进行睡眠评分时的过程。 建议的决策和备选决策的可能性估计为 用于量化建议分数中的置信度。系统将标记 信心分数低，需要通过人际互动进行修正。初步测试 表明机器和人类之间的一致性程度是相似的 两个人类得分手之间的关系。将开发软件工具以 检验算法参数对阶段得分的影响 决定。一个正常和测试的训练和测试集的初始数据库 将建立呼吸暂停睡眠记录，算法将被 在此数据上进行了优化和测试。第二阶段将优化参数和 在更大的数据集上验证系统的其他诊断和年龄 分组、开发在线、实时功能，并扩展决策 模型，以促进睡眠医学的进一步研究。

DESCRIPTION:(Adapted from applicant's abstract): Implementation and validation of a computerized expert system sleep scoring is proposed. Such a system would increase productivity of sleep laboratories, and increase the quality while reducing the cost of sleep diagnostic procedures. Phase I research will focus on the basic algorithms for automated sleep stage scoring. The algorithms are based on Bayesian decision theory and follow the steps of the human expert in the decision making process when scoring sleep by the Rechtschaffen-Kales standard. Likelihood estimates of the suggested and alternative decisions will be used to quantify confidence in suggested scores. The system will flag low confidence scores to be revised by human interaction. Initial tests show that the level of agreement between machine and human is similar to that between two human scorers. Software tools will be developed to test the effect of the parameters of the algorithms on stage score decisions. An initial data base of training and test sets of normal and apneic sleep recordings will be established and the algorithms will be optimized and tested on this data. Phase II will optimize parameters and validate the system on larger data sets for other diagnostic and age groups, develop on-line, real-time capabilities, and extend the decision model to facilitate further research in sleep medicine.