Aiming at the problem that the unbalanced distribution of data in sleep electroencephalogram(EEG) signals and poor comfort in the process of polysomnography information collection will reduce the model's classification ability, this paper proposed a sleep state recognition method using single-channel EEG signals (WKCNN-LSTM) based on one-dimensional width kernel convolutional neural networks(WKCNN) and long-short-term memory networks (LSTM). Firstly, the wavelet denoising and synthetic minority over-sampling technique-Tomek link (SMOTE-Tomek) algorithm were used to preprocess the original sleep EEG signals. Secondly, one-dimensional sleep EEG signals were used as the input of the model, and WKCNN was used to extract frequency-domain features and suppress high-frequency noise. Then, the LSTM layer was used to learn the time-domain features. Finally, normalized exponential function was used on the full connection layer to realize sleep state. The experimental results showed that the classification accuracy of the one-dimensional WKCNN-LSTM model was 91.80% in this paper, which was better than that of similar studies in recent years, and the model had good generalization ability. This study improved classification accuracy of single-channel sleep EEG signals that can be easily utilized in portable sleep monitoring devices.
针对睡眠脑电（EEG）信号数据不均衡分布以及多导睡眠图采集过程中舒适性差，从而降低了模型分类能力的问题，本文提出一种基于一维宽核卷积神经网络（WKCNN）和长短时记忆网络（LSTM）的单导EEG信号睡眠状态识别方法（WKCNN-LSTM）。首先，通过小波去噪，并以合成少数过采样技术（SMOTE）与托梅克联系对（Tomek）联合的算法（SMOTE-Tomek）对原始睡眠EEG信号进行预处理；其次，以一维睡眠EEG信号作为模型的输入，利用WKCNN提取频域特征并抑制高频噪声；然后，利用LSTM层挖掘时域特征；最后，全连接层采用归一化指数函数实现睡眠状态识别。实验表明，本文一维WKCNN-LSTM模型的分类准确率为91.80%，分类效果优于近年的同类研究，并且该模型具有良好的泛化性能。本研究不仅提高了单导睡眠EEG信号的分类准确率，也有利于促进便携式睡眠监测设备性能的提高。.
Keywords: Class imbalance; Convolutional neural networks; Long-short-term memory networks; Single-channel electroencephalogram signals; Sleep state recognition.