Estrada E, Nazeran H, Barragan J, Burk J R, Lucas E A, Behbehani K
Dept. of Electr. & Comput. Eng., Texas Univ., El Paso, TX, USA.
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:2458-61. doi: 10.1109/IEMBS.2006.260075.
Sleep is a natural periodic state of rest for the body, in which the eyes are usually closed and consciousness is completely or partially lost. In this investigation we used the EOG and EMG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by expert sleep specialists based on RK rules. Differentiation between Stage 1, Awake and REM stages challenged a well trained neural network classifier to distinguish between classes when only EEG-derived signal features were used. To meet this challenge and improve the classification rate, extra features extracted from EOG and EMG signals were fed to the classifier. In this study, two simple feature extraction algorithms were applied to EOG and EMG signals. The statistics of the results were calculated and displayed in an easy to visualize fashion to observe tendencies for each sleep stage. Inclusion of these features show a great promise to improve the classification rate towards the target rate of 100%
睡眠是身体自然的周期性休息状态，在此状态下眼睛通常闭合，意识完全或部分丧失。在本研究中，我们使用了从10名接受整夜多导睡眠图检查的患者身上采集的眼电图（EOG）和肌电图（EMG）信号，其睡眠阶段由专业睡眠专家根据RK规则确定。当仅使用脑电图衍生的信号特征时，1期、清醒期和快速眼动期之间的区分对训练有素的神经网络分类器区分这些类别构成了挑战。为应对这一挑战并提高分类率，从EOG和EMG信号中提取的额外特征被输入到分类器中。在本研究中，两种简单的特征提取算法被应用于EOG和EMG信号。计算结果的统计数据并以易于可视化的方式显示，以观察每个睡眠阶段的趋势。纳入这些特征显示出极大的希望，有望将分类率提高到目标率100%
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