Beijing Institute of Technology, Beijing, China.
Beijing Institute of Technology, Beijing, China; Technical University of Munich, Munich, Germany.
Artif Intell Med. 2022 May;127:102279. doi: 10.1016/j.artmed.2022.102279. Epub 2022 Mar 9.
This work proposed a novel method for automatic sleep stage classification based on the time, frequency, and fractional Fourier transform (FRFT) domain features extracted from a single-channel electroencephalogram (EEG). Bidirectional long short-term memory was applied to the proposed model to train it to learn the sleep stage transition rules according to the American Academy of Sleep Medicine's manual for automatic sleep stage classification. Results indicated that the features extracted from the fractional Fourier-transformed single-channel EEG may improve the performance of sleep stage classification. For the Fpz-Cz EEG of Sleep-EDF with 30 s epochs, the overall accuracy of the model increased by circa 1% with the help of the FRFT domain features and even reached 81.6%. This work thus made the application of FRFT to automatic sleep stage classification possible. The parameters of the proposed model measured 0.31 MB, which are 5% of those of DeepSleepNet, but its performance is similar to that of DeepSleepNet. Hence, the proposed model is a light and efficient model based on deep neural networks, which also has a prospect for on-device machine learning.
这项工作提出了一种基于单通道脑电图（EEG）在时间、频率和分数阶傅里叶变换（FRFT）域特征的新型自动睡眠阶段分类方法。双向长短期记忆被应用于所提出的模型中，以根据美国睡眠医学科学院的自动睡眠阶段分类手册训练模型来学习睡眠阶段的过渡规则。结果表明，从分数阶傅里叶变换的单通道 EEG 中提取的特征可能会提高睡眠阶段分类的性能。对于具有 30 秒时窗的 Sleep-EDF 的 Fpz-Cz EEG，在 FRFT 域特征的帮助下，该模型的整体准确率提高了约 1%，甚至达到了 81.6%。因此，这项工作使得 FRFT 在自动睡眠阶段分类中的应用成为可能。所提出的模型的参数为 0.31MB，仅为 DeepSleepNet 的 5%，但其性能与 DeepSleepNet 相似。因此，所提出的模型是一种基于深度神经网络的轻量级高效模型，也有望用于设备上的机器学习。
