1. For scientific and clinical requirements the present objective is a robust automatic online algorithm to detect rapid eye movement (REM) sleep from single channel sleep EEG data without using EMG or EOG information. 2. For data preprocessing 20 seconds time periods of the continuous EEG activity are digitally filtered in 7 frequency bands. Then the RMS values of these filtered signals are calculated along segments of 2.5 seconds. The resulting matrix of RMS values is representing information on the power of the signal localized in time and frequency and serves as input to an artificial neural network. A pooled set of EEG data together with the corresponding manual evaluation of the recordings was used in the training process. 3. Afterwards more than 90% of the time periods not belonging to the training set could be correctly labeled into REM and nonREM periods. In comparison to an older algorithm based on RMS values calculated along segments of 20 seconds, the error rate could be reduced by 20%.

译文

1.对于科学和临床要求,本目标是一种强大的自动在线算法,无需使用EMG或EOG信息即可从单通道睡眠EEG数据中检测快速眼动(REM)睡眠。 2.对于数据预处理,在7个频段中对20秒钟连续EEG活动的时间段进行数字滤波。然后,这些滤波信号的RMS值将沿着2.5秒的时间段进行计算。 RMS值的结果矩阵表示有关时间和频率上本地化的信号功率的信息,并用作人工神经网络的输入。在训练过程中使用了一组合并的EEG数据以及相应的录音手动评估。 3.之后,可以将不属于训练集的90%以上的时间段正确地标记为REM和非REM周期。与基于沿20秒段计算出的RMS值的旧算法相比,错误率可以降低20%。

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