Lee Woonghee, Kim Younghoon
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:183-186. doi: 10.1109/EMBC.2018.8512219.
Traditional manual scoring of the entire sleep for diagnosis of sleep disorders is highly time-consuming and dependent to experts experience. Thus, automatic methods based on electrooculography (EOG) analysis have been increasingly attracted attentions to lower the cost of scoring. Such computeraided diagnosis of sleep disorders are usually based on the 6 scores, wake (W), sleep status (S1-S4) and REM by labelling every 30-second long EOG records. This paper presents an automatic scoring method of sleep stages by using the recent advancements in deep learning. We also suggest an interactive scoring scheme to enable the doctors of practitioners to give feedback by correcting errors and improve the accuracy of scoring as well as diagnosis of sleep disorders.
传统的通过人工对整个睡眠过程进行评分以诊断睡眠障碍的方法非常耗时，且依赖专家经验。因此，基于眼电图（EOG）分析的自动方法越来越受到关注，以降低评分成本。这种睡眠障碍的计算机辅助诊断通常基于6种评分，即清醒（W）、睡眠状态（S1 - S4）和快速眼动（REM），通过对每30秒长的EOG记录进行标注来实现。本文提出了一种利用深度学习最新进展的睡眠阶段自动评分方法。我们还提出了一种交互式评分方案，使医生或从业者能够通过纠正错误给出反馈，提高评分准确性以及睡眠障碍的诊断准确性。
