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首页> 外文会议>World Congress on Intelligent Control and Automation;WCICA 2012 >Automatic sleep stage classification based on ECG and EEG features for day time short nap evaluation
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Automatic sleep stage classification based on ECG and EEG features for day time short nap evaluation

机译：基于ECG和EEG功能的自动睡眠阶段分类，用于白天短时午睡评估

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In this study, the Electrocardiogram (ECG) and Electroencephalogram (EEG) data recorded during day time short nap were analyzed. The ultimate purpose is to find out effective ECG features combined with usual EEG features for sleep stage determination during day time nap. Firstly, the ECG data was pre-processed in order to eliminate artifacts. After preprocessing, the second-order derivative of the ECG signal was calculated and clustered into two classes by K-means method. The peak positions of R wave were detected. Secondly, the Heart Rate Variability (HRV) was calculated according to the RR intervals (RRIs). Features of HRV of ECG were extracted in time-domain and frequency-domain. The redundant features were removed by the rough set method. Finally, the extracted features from the HRV of ECG were combined with the usual EEG features for sleep stage determination. The sleep stages including stage awake, stage 1 and stage 2 were distinguished by using Support Vector Machine (SVM). The obtained result indicated that the extracted ECG features improved the sleep stage classification accuracy.
机译：在这项研究中，分析了白天小睡期间记录的心电图（ECG）和脑电图（EEG）数据。最终目的是找出有效的ECG功能和通常的EEG功能，以在白天小睡时确定睡眠阶段。首先，对ECG数据进行预处理以消除伪影。经过预处理，计算出ECG信号的二阶导数，并通过K-means方法将其分为两类。检测到R波的峰值位置。其次，根据RR间隔（RRI）计算心率变异性（HRV）。在时域和频域提取心电图的HRV特征。冗余特征通过粗糙集方法去除。最后，将从心电图的HRV中提取的特征与通常的EEG特征相结合，以确定睡眠阶段。睡眠阶段包括清醒阶段，阶段1和阶段2通过使用支持向量机（SVM）进行区分。获得的结果表明，提取的心电图特征提高了睡眠阶段分类的准确性。

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