基于CNN-BiLSTM的自动睡眠分期算法

睡眠分期是睡眠数据分析的基础, 针对目前睡眠分期存在的依赖人工提取、人工判别效率低、自动睡眠分期准确率不高等问题, 本文研究模型是基于卷积神经网络和双向长短时记忆神经网络2个深度学习神经网络相结合的, 利用脑电信号来进行自动睡眠分期的模型方法. 算法能提取得到原始脑电信号的梅尔频谱, 利用卷积神经网络和双向长短时记忆神经网络进行时频域的特征提取, 卷积神经网络能够提取睡眠信号高级特征, 双向长短时记忆神经网络结合睡眠数据不同时期的关联性, 提高自动睡眠分期的准确率. 实验结果表明, 本文方法在Sleep-EDF数据集的3种状态睡眠分期任务中取得89.0%的平均准确率. 与传统的基于统计规则的分期模型相比, 本文模型的准确率更高, 且简单高效, 泛化性能更好. 本文算法适用于非线性、不稳定、有幅度起伏变动的脑电信号, 有效提高了自动睡眠分期模型结果的准确率, 对现代睡眠医学、睡眠障碍等分析研究具有一定的实用价值.

Sleep Staging Classification Based on CNN-BiLSTM

Sleep staging is the basis of sleep data analysis. Given the dependence on manual extraction, the inefficiency of manual classification, and the inaccuracy of automatic sleep staging of current sleep staging methods, this paper proposes a method that combines two deep-learning neural networks, namely the convolutional neural network (CNN) and the bidirectional long-short memory neural network (BiLSTM), and uses electroencephalogram (EEG) data to conduct automatic sleep staging. This algorithm can extractmelspectrograms toobtain the original EEG dataand uses CNN and BiLSTM to extractfeatures in the time domain and the frequency domain. CNN can extract the high-level features of sleep signals, and BiLSTM can improvethe accuracy of automatic sleep staging when combinedwith the correlation of sleep data of different stages. The experimental results show that the proposed methodachievesan average accuracy of 89.0% in the three-state sleep staging task on the Sleep-EDF dataset. Compared with the traditional staging model based on statistical rules, this model is simpler, more accurate, and more efficient and has better generalization performance. The proposed algorithm is suitable for nonlinear, unstable, and non-stationary EEG data and effectively improves the accuracy of the results of the automatic sleep staging model. It possesses practical value in modern sleep medicine, sleep disorders, and other research.